Report of the Joint Cognitive Human Machine System Group (JCHMS)

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Report of the Joint Cognitive Human Machine System Group (JCHMS)

62ND ANNUAL CONFERENCE, Montego Bay, Jamaica, 8-12 May 2023

WP No. 165

Report of the Joint Cognitive Human Machine System Group (JCHMS)

Presented by JCHMS

 

Summary

The future influence of new technologies such as Machine Learning and Artificial Intelligence will pose new challenges to the working environment in Air Traffic Control.

Under the umbrella of IFATCA, a group of motivated people has started to discuss and do research on these challenges. This group has produced several conferences papers and has finalised a draft guidance material. The aim being that this guidance material will serve the Federation in the future to assist in educating its membership and contribute to the regulation, certification, and ongoing research initiatives. This paper should be a major contribution to the discussion on the future of technology.

Introduction

1.1. A group of dedicated professionals has started to tackle this important subject since over a year.

1.2. Composed of Ms Nora Berzina (MUAC ATCO, EGATS), Dr. Anthony Smoker (EASA representative IFATCA, GATCO UK), Dr. Stathis Malakis (SESAR representative ATCO, Greece), Mr. Andrea Potti (EASA representative IFATCA, Italy), Mr. Tom Laursen (former EVP Europe, Denmark), have meet 30 times and were joined by Dr. Marcello Scala (Italy) and Sergio Velotto (Italy) at the later stage of the work. Mr. Marc Baumgartner (SESAR/EASA Coordinator, Switzerland) has acted as coordinator of the group.

1.3. The aim of the group is to establish guidance material to be used in the future by IFATCA, the air traffic controllers and to engage with the outside world.

1.4. Several papers and presentation have been developed and presented to scientific reviews and conferences.

1.5. Four annexes reflect the published papers to date.

Discussion

2.1. Setting the Scene

Today’s Air Traffic Management (ATM) system is heavily reliant on the interdependence between designers, technology, and the human operator. Although there have been several periods of time where the demise of the human operator has been predicted, it hasn’t materialized in significant changes to the designer, technology, and human operator equation. Especially in Europe, recent discussions, have created a push for a change of the role of the human operator. It seems that a system where the human operator is no longer necessary is attractive and is promoted by many stakeholders. The push and political agenda will probably continue to favor the notion of full automation as defined in the many different descriptions of automation.

There is an alternative to this approach. An approach where it’s important to try understanding the totality of the system before we try change the parts within the system. It assumes that the Aviation system is a complex system that is dynamic, adaptive, and evolving and where context, self-organization and emergence is always present. Taking this view leads us to a different path for designing today’s aviation system. An approach based on non-linearity, circular design, and the understanding of how the parts relate to each other. This reflects the centrality of a system’s approach to design and operation in the JCHMS narrative.

IFATCA is of the opinion that adopting the path of JCHMS and the thinking that the ATM system performs best by designing and integrating technology into the equation of designers, technology, and a human operator.

Changes in the ATM domain and at the ATM /CNS front are of permanent nature and challenges of research, development and transition to introduce these changes are daily life for ANSPs and their Staff. Be it Air Traffic Controllers, Technicians, Engineers, Managers and Decision Makers. The so-called New Technologies leading to digitisation or digitalisation, including Artificial Intelligence (AI) and Machine Learning (ML) are finding their ways into the ATM working environment.

Staying in control is important. The introduction of AI and ML will introduce new uncertainties that may reduce the span of control that we have. It’s challenges that needs to be dealt with and we think that using the JCHMS approach will ensure that.

IFATCA’s view on how to design and integrate new technology to the Aviation system is based on the following assumptions:

  1. The more advanced a control system is, the more crucial may be the contribution of the human operator.
  2. We need technology that makes us smarter – not smarter technology.
  3. New technology needs to be designed by designers and operators with overall system goals in mind.

There is a need to move away from the idea of removing the human operator through automation AI and ML, and towards joint ‘thinking’ model, where the designer, the technology and the human operator is the model for future technological solutions. The aim of the joint view is to combine the strengths of humans and technology so that they amplify each other and that the ability to respond and anticipate surprises is increased or at least maintained.

For IFATCA it’s paramount that we build on the successes of the past, where staying in control and designing systems to use the combined strength of the human and the technology. Fortunately, service-providers are realizing that to get the best out of any new system development, it has to be developed with current operational personnel. Within IFATCA, “operational controllers” are working hard to ensure that the operational voice is heard at the highest level in ICAO – the Air Navigation Commission – through regional planning and implementation groups into regional activities.

In this narrative, the JCHMS controllers can be agents of change – a catalyst for innovative ways by which the network and system can deliver service provision in a manner that retains its ability to achieve its goals in a sustainable manner.

Linked to the regulatory and certification challenges, a lot of the new technology will have to be interwoven into the existing architecture and will create new challenges, surprises and will not escape the rough journey of increase in technological systems in ATM. IFATCA doubts that despite the provisions of ICAO through the introduction of such mechanisms as the Manual of RNAV etc, controllers have yet been able to exploit these in the sense of ‘control’- the practice of active control of aircraft in achieving performance goals around capacity and demand. There is scope to radically enhance service provision through amplifying the potential of joint human-technology designs that exploit this potential. Yet retain the space for humans to retain control bringing with it the capacity to adapt.

Air Traffic Controllers are at the front when it comes to using new equipment. Their daily working environment is one of constant change, therefore IFATCA continues to develop guidance, information, and educational material regarding new technologies, be it increased automation of the housekeeping tasks or a digitized working environment.

In order to carry out this challenging task, IFATCA has created a team called the Joint Cognitive Human Machine System (JCHMS) group, which has produced several papers for international conferences, and is working at guidance material for IFATCA.

The papers submitted cover different facets of the topic.


2.2. Reducing the Gap Between Designers and Users, Why are Aviation Practitioners Here Again?

IFATCA was invited to present the submitted working paper to conference. SESAR EASA coordinator was able to present the paper at the International Conference on Cognitive Aircraft Systems (ICCAS) . (Annex I)

Here is a summary of this paper:

Our approach to designs for human-system integration resulted in designs with reduced margins to manage and work with uncertainty and surprise within the work systems. This paper argues that technological designs often underperform compared to the promised benefits delivered. The reason for this is principally because designs have been based on a strategy where practitioners e.g., ATCOs, pilots etc, are expected to take over in abnormal conditions – the so called ‘left-over’ design strategy’ or the. There is a need to retain the human in the system to ‘complete the design, so as to adapt to the situations that designers never anticipated’. We argue that the need to change this philosophy of design is necessary, because we cannot think of engineering as a design without considering the people and the organisations that go with it. The operating environments of interest here, complex macro- cognitive work designs, are what is mentioned as socio-cognitive systems and are confronted with the challenge of digitisation and integration of artificial intelligence.


2.3. Challenges from the Introduction of Artificial Intelligence in the European Air Traffic Management System

IFATCA’s conference paper for the International Federation of Automatic Control was accepted as a conference paper which was published under the following link: https://www.sciencedirect.com/science/article/pii/S240589632201730X

Here is a summary of this paper:

The Air Traffic Management (ATM) system can be defined as a “Joint Cognitive System” of people, teams, and artifacts that adapts to the challenges and demands posed by familiar and unfamiliar situations in a dynamically evolving operational context. In the era of digitalization and Big Data we live, an incremental modernization of the ATM system is expected in the coming years with the pervasive implementation of Artificial Intelligence (AI) and Machine Learning (ML). In this paper, we present the findings from an initial attempt to detect and document the fundamental challenges of the introduction of AI, in the European ATM system through the lens of Cognitive Systems Engineering paradigm. We also discuss how these challenges give rise to difficult to resolve safety and performance related patterns in the ATM system.


2.4. Single European Sky and Resilience in ATM – Can This be a “Win-Win” for the Aviation Industry – the IFATCA Input

IFATCA submitted a paper to the FABEC Resilience Conference. EVP European and Stathis Malakis were able to present the ideas around Resilience in ATM.

Here is a summary of this paper:

In this paper we argued that SES has a large share of responsibility in institutionalizing financial and performance fragility to the European ANSPs, we provided a framework of possible solutions to counteract both financial and performance fragility and we stressed the importance of reframing the SES Environment performance indicator to embrace the interdependencies between several performance areas. Thus, by creating a systemic approach to manage the ATM contribution to decarbonization IFATCA claims that this could be a win- win situation. The research and development of the SESAR projects will have to focus on considering the aspects that we have highlighted so far. Although we are aware of the need to aim for an ATM system that is increasingly attentive to emissions and environmental issues, it is necessary to ensure that these objectives do not conflict with the highest safety standards. Furthermore, it is necessary that any new procedures studied are not going to provide additional burdens to the personnel involved with new tasks that could, in an already particularly congested situation, further aggravate the work of the operating personnel.


2.5. A Framework for Supporting Adaptive Human-AI Teaming in Air Traffic Control

IFATCA submitted a paper to the 25th International Conference On Human-Computer Interaction.

Here is a summary of this paper:

In recent years, the growth of cognitively complex systems has motivated researchers to study how to improve these systems’ support of human work. At the same time, there is a momentum for introducing Artificial Intelligence (AI) in safety critical domains. The Air Traffic Control (ATC) system is a prime example of a cognitively complex safety critical system where AI applications are expected to support air traffic controllers in performing their tasks. Neverthe- less, the design of AI systems that support effectively humans poses significant challenges. Central to these challenges is the choice of the model of how air traf-fic controllers perform their tasks. AI algorithms are notoriously sensitive to the choice of the models of how the human operators perform their tasks. The design of AI systems should be informed by knowledge of how people think and act in the context of their work environment. In this line of reasoning, the present study has set out to propose a framework of cognitive functions of air traffic controllers that can be used to support effectively adaptive Human – AI teaming. Our aim was to emphasize the “staying in control” element of the ATC. The proposed framework is expected to have meaningful implications in the design and effective operationalization of Human – AI teaming projects at the ATC Operations rooms.


2.6. Guidance Material

The above outlined papers have been used a building block for the Guidance Material (GM). The GM has reached a level of maturity which allowed the group to send it around and collect answers from external “friendly readers”. The friendly readers are ATCOs, academics, members of the EB and regulator. Currently the JCHMS group is collecting the answers and will produce a further version before publication.

The content of the paper will be explained in form of information sessions. Any possible policy changes will be proposed to conference 2024.


3. Conclusions

The so-called New Technologies leading digitalization, including AI and ML are finding their ways into the ATM working environment. Whereas lot of expectation is linked to a so-called technology hype introduction of new technology will have to follow the path of introducing new technological component into a running ATM system. This paper presents an outline of the results of the JCHMS group that is trying to influence designing and integrating technology into the equation of designers, technology, and a human operator. The GM material and the supporting documents (i.e., conference papers) aim to assist Air Traffic Controllers, Operational Supervisors, Flow Controllers and Flight Information Officers integrate technology in the various forms of new intelligent, autonomous systems, automation and AI/ML that works in partnership with the human operator in the operations rooms. In our approach Technology is collective noun meaning variously intelligent systems, automation, autonomous systems, AI/ML systems and digital cognitive assistants. We propose a set of generic principles and an iterative process fielding technology system in the OPS rooms based on Joint Cognitive Human Machine Systems. In the GM we also propose a set of practical and adoptive principles based on the tenets of Cognitive Systems Engineering. The aim being that this guidance material will serve the Federation in the future to assist in educating its membership and contribute to the regulation, certification, and ongoing research initiatives.

Annex I – Reducing the gap between designers and users, Why are aviation practitioners here again?

(Laursen, T., Smoker, A.J., Baumgartner, M., Malakis, S. & Berzina, N.,  supported by the IFATCA JHMS group, International Federation of Air Traffic Controllers Associations, Montreal, Canada, tom.laursen@ifatca.org)

Introduction

Our approach to designs for human-system integration resulted in designs with reduced margins to manage and work with uncertainty and surprise within the work systems. This paper argues that technological designs often underperform compared to the promised benefits delivered. The reason for this is principally because designs have been based on a strategy where practitioners e.g., ATCOs, pilots etc, are expected to take over in abnormal conditions – the so called ‘left-over’ design strategy’ or the (Inagaki, T, 2014, p235)). Inagaki also argues, citing Rasmussen & Goodstein, that there is a need to retain the human in the system to ‘complete the design, so as to adapt to the situations that designers never anticipated’ (Inagaki, 2014, p235) We argue that the need to change this philosophy of design is necessary, as Boy argues: “We cannot think of engineering a design without considering the people and the organisations that go with it” Boy argues (Boy, 2020). The operating environments of interest here, complex macro-cognitive work designs, are what Boy refers to as socio-cognitive systems (Boy, 2020) and are confronted with the challenge of digitisation and integration of artificial intelligence.

Uncertainty and surprises will always be an element of complex systems,

Complexity research (Flach, 2014; Heylighen, Cilliers & Gershenson 2007, Cilliers, 2000) and the study of chaotic dynamics have demonstrated that uncertainty and surprise are fundamental aspects of the world around us (Eisenberg, Seager, Alderson, 2019; Lanir, 1983). Instead of an ordered system, such as machines, the aviation system is a complex system whose properties emerge from nonlinear interactions of numerous different agents. These interactions, and the interplay between them, create uncertainty and fundamental surprises (Eisenberg, Seager, Alderson, 2019. Woods et al, 2010, Lanir, 1983) which need to be managed in ways where as far as possible lead to being able to stay in control (McDaniel, Jr., Driebe, 2006).

The human or the machine?

Today’s aviation system consists of many different actors and agents that affect the ability to respond to uncertainty and surprises. There is a political level, an organisational level, a social dimension, training of practitioners, and numerous others. Knowing this, the only model of a human system is the system itself. We assume that there is a basic shared model of operation such as common ground in joint activity (Klein et al, 2005) between different actors. The basic model of operation consists of two interdependent processes. One is the process of preparing the other is the constant real-time adaptive capacity – that is the capacity to adapt to situational and fundamental surprises (Eisenberg et al 2019) and performance variability whilst sustaining production and system goals, which practitioners deliver principally.

The process of preparing entails procedures, checklists, runway signs, maps of the air, lightning, technology, the allocation and securing of resources, designing new technology and many other activities. Organisational adaptive capacity is developed through training, experience, the ability of humans to anticipate, pattern recognition, mental models, the ability to respond in real-time and many more skills needed to respond to changes, uncertainty and surprises that we know will occur.

Designing technology to handle uncertainty and surprises requires that the designers of technology do so with this characteristic of ‘work’ in mind. In order to do this designers, need a complete understanding of the uncertainty and surprises that will emerge within the aviation system. However, this requires perfect knowledge. As we all know, perfect knowledge is never available e.g., Air traffic controllers (ATCOs) quite often improvise in situ to meet the challenges of traffic imposed by novel events, unfortunate actions and shortcomings of the work system. In the ATM system balancing efficiency and thoroughness, involves making improvisations and departing from existing procedures under conditions of time pressure, uncertainty, and high workload. The rapid expansion of information technology has increased the amount of information presented to ATCOs without any assistance in how to make sense or to anticipate the current situation or future trends. Quite frequently ATCOs are dealing with a complex and dynamic environment that requires them to attend to multiple events, anticipate aircraft conflicts and comprehend or make sense of evolving scenarios. Experience has shown that staying in control when exposed to surprises is the main challenge in today’s very safe aviation system. If you can eliminate uncertainty and surprises, you can remove real-time control of the system by the human. Todays’ rare accidents are characterised by complexity and surprise rather than by broken parts or components. The latest prominent example is the 737 max accidents (Nicas et al, 2019). Boeing management decided that the designers of the technology could foresee all possible uncertainty and decided to keep the human practitioners out of the loop. It is unrealistic, to assume that uncertainty and surprise can be eliminated. This leads to a system requirement for designs to have the human actively involved in the control functions of the system.

Two different mental models

Historically, the aim for designing complex technical systems has been to replace or limit the authority to act of the human practitioner in real time operational control and management of the systems activities. Another design approach has been to partially remove the human practitioner and create a strict task-sharing environment in which automation deals with routine tasks and events, while the human is exclusively responsible for rare high complexity situations. In essence, these system activities at the micro level are the work i.e., the purposeful activity of the real-time system. Thus, this perspective of work reduces the purposeful activity as it reduces the involvement of the human practitioner. In particular it reduces the ability to respond to uncertainty and surprise.

This approach is driven by the idea that it is possible to substitute the human practitioner with technology that includes prepared responses to uncertainty and surprises. Lisanne Bainbridge describes this approach, in her 1988 paper (Bainbridge, 1988): The designer’s view of the human practitioner may be that the practitioner is unreliable and inefficient. so should be eliminated from the system.

An alternative approach, is where systems are designed to be able to support management and adaptation of uncertainty and surprises by collaboration and co-allocation between technology and the human practitioner (Bradshaw, 2011). This approach has been called the joint cognitive approach (JCA) (Hollnagel and Woods, 2005) and is based on the notion that the human practitioner stays in control and that we design for the human practitioner to know what the technology is doing, a design that emphasises common ground.

Klein extends and amplifies this perspective further in the two views in the table below (Klein 2022) that represent designers and end users’ perspectives:

Table 1: Differing design requirements of system designers and system end users (Klein, 2022)

Taking the designer’s view there are some caveats that we have to be aware of. Again, Bainbridge describes it in this way:

  • One, that designer assumptions can be a major source of operating problems and,
  • The second problem is that the designer who tries to eliminate the practitioner, the left-over functions, still leaves the practitioner to do the tasks which the designer cannot think how to automate.
  • An additional problem is that the most successful automated systems, with rare need for manual intervention, may need the greatest investment in human practitioner training.

Taking the joint cognitive and the human system integration approaches (Hollnagel, Woods, 2005; Boy, 2020) are extant philosophies for collaboration between technology and the human which retain control in real-time operation.

Design for collaboration between technology and humans – design a social cognitive system

How do we meaningfully bring technology and social actors – the designer and the user – together to match a complex world with its inherent complex adaptive solutions that are playing out in real–time?

The challenge becomes, in a complex world compared to a complicated word, , how do we reconcile the different mental models of the different actors to create designs that enhance adaptive capacity?

The dualism of the two different mental models becomes more complicated when considering design and the engineering of the design, for complex socio-technical systems.

Design for complex socio-technical systems, can be seen as an exercise in conflicting value systems (Baxter & Somerville, 2011, citing Land 2009). For example:

  • Design values with a fundamental commitment to humanistic principles: the designer aiming to improve the quality of working life and job satisfaction of those operating in and with the system.
  • Managerial values: the principles of socio-technical design are focused on achieving the company or organisational objectives especially economic ones

These two sets of values conflict. And we argue that this tension can contribute to a decrement in system adaptive capacity as well as adding costs to the system’s effectiveness and its ability to achieve system production, goals and objectives.

One of the driving arguments for automation is that costs of production are reduced because there are fewer human costs – be it training, the reliability of the practitioner, the inefficiency of the practitioner. Designs that seek to optimise managerial values can have the effect – intentional or otherwise -to privilege the managerial objectives and in doing so constrain the humanistic design. The consequences of this are that the practitioners’ degrees of freedom are reduced; buffers and margins are impacted in ways that limit the ability of the system to maintain and sustain adaptability when confronted with uncertainty and surprise events and thereby making the system less effective. Additionally, increasing the distance between the practitioner, and the system reduces the practitioner’s ability to intervene in case of unexpected events:

Work changes. When work changes there are consequences on the practitioner’s ability to create strategies that can exploit system characteristics of agility and flexibility, in other words adaptive capacity. Boy (Boy, 2020) refers to this as a form of smart integration: designing for innovative complex systems – that exploit the ability to understand increasing complexity. This means embracing complexity. What are we designing for?

A design that embraces complexity will adopt the opposite of the reductionist view – which means reducing or eliminating the effects of complexity, by eliminating or reducing the role of the human. As opposed to designs that embrace and design for complexity by matching emerging system behaviours with creative emergent human real time responses.


Conclusion

In this paper we argue that we need to move towards designing a socio-cognitive system. This is proposed as a way forward to reduce the distance between practitioners and designers so that designs incorporate joint activity that supports common ground.
To make that possible, we must embrace complexity, uncertainty and surprises rather than trying to eliminate it. In doing so the role of the human practitioner is recognised and sustained, which permits more efficient and effective operation in real-time. Furthermore, such an approach can lead to maintaining job satisfaction, practitioner involvement and the real-time learning and adjustments of patterns of activity associated with complexity, uncertainty and surprises.

One of the means to achieve a constructive approach to the design of effective and meaningful human-system integration is through new ways of working together. These need to be institutionalised and embedded by the Regulator. In the recent Boeing episode, the manufacturer was doing the regulators job (Nicas, J. et al, 2019).

Further areas for consideration are a coherent transition plan should be derived to identify the needs of management and the human practitioner in complex socio-cognitive systems. Another question is whether we are deceived by the optimistic predictions of costs saved by tools and method of operations without the human practitioner.


References

Bainbridge L (1983): Ironies of automation. Automatica 19:775–779.

Baxter, G., & Somerville, I. (2011). Socio-technical systems: from design methods to systems engineering. Interacting with computers Vol.23: 4-17.

Boy G, (2020). Human-systems integration. CRC Press.

Bradshaw, J., Feltovich, P., & Johnson. (2011) . Human-agent interaction. In G. Boy (Ed), The handbook of human-machine interaction p. 283-303 Ashgate.

Cilliers, P. (2000), Complexity and postmodernism: understanding complex systems. Routledge.

Eisenberg, D., Seager, T., Alderson, D.L. (2019). Rethinking resilience analytics. Risk Analysis DOI: 10.1111/risa.13328.

Flach, J. (2012), Complexity: learning to muddle through. Cogn Tech Work (2012) 14:187–197. DOI 10.1007/s10111-011-0201-8.

Heylighen, F., Cilliers, P., Gershenson, (2007). Complexity and Philosophy. In Ed. J. Bogg, R. Geyer. Complexity, Science and Society (1ST Edition). CRC Press.

Hollnagel E, Woods D (2005): Joint Cognitive Systems: Foundations of Cognitive Systems Engineering. CRC Press, Taylor and Francis.

Inagaki, T. (2014). Human-machine co-agency for collaborative control. In Eds, Yoshiyuki, Sankai; Kenji Suzuki, Yasuhisa Hasegawa: Cybernics: fusion of human, machine an information systems. Springer.

Klein, G. (2022). The war on expertise. Novellus. https://novellus.solutions/insights/podcast/war-on-expertise-how-to-prepare-and-how-to-win/.

Klein, G., Feltovich, P.J., Bradshaw, J.M., Woods, D.D. (2005). Common ground and coordination inf joint activity. In Eds W. B. Rouse & K.R. Boff, Organisational Simulation: Wiley.

Lanir, S. (1983). Fundamental surprises. Tel Aviv: Centre for strategic studies.

McDaniel P.R Jr., & Driebe D.J. (2006): Uncertainty and Surprise in Complex Systems: Questions on Working with the Unexpected. Springer Publishing.

Nicas J, Kitroeff N, Gelles, D, Glanz J. (2019). Boeing built deadly assumptions into 737 Max, blind to a late design change [Internet]. New York: The New York Times; 2019 Jun 1 [cited 2019 Oct 17]. Available from https://www.nytimes.com/2019/06/01/business/boeing-737-max-crash.html

Woods, D.D. (2010). Fundamental surprise. In Ed.D.D. Woods, S.W.A Dekker, R. Cook, L, Johannesen, N. Sarter. Behind human error (2nd Edition). CRC Press.

Annex II – Challenges from the Introduction of Artificial Intelligence in the European Air Traffic Management System

Malakis Stathis*. Baumgartner Marc*. Berzina Nora*. Laursen Tom *. Smoker Anthony*. Poti Andrea*. Fabris Gabriele*.

* Supported by the IFATCA Joint Cognitive Human Machine System (JCHMS) group. International Federation of Air Traffic Controllers Associations, Montreal, Canada. (email: stathis.malakis@gmail.com)

Abstract

The Air Traffic Management (ATM) system can be defined as a “Joint Cognitive System” of people, teams, and artifacts that adapts to the challenges and demands posed by familiar and unfamiliar situations in a dynamically evolving operational context. In the era of digitalization and Big Data we live, an incremental modernization of the ATM system is expected in the coming years with the pervasive implementation of Artificial Intelligence (AI) and Machine Learning (ML). In this paper, we present the findings from an initial attempt to detect and document the fundamental challenges of the introduction of AI, in the European ATM system through the lens of Cognitive Systems Engineering paradigm. We also discuss how these challenges give rise to difficult to resolve safety and performance related patterns in the ATM system.

Keywords: Cognitive Systems Engineering, Joint Cognitive Systems, Air Traffic Control, Air Traffic Management, Artificial Intelligence, Machine Learning, Digitalization.


1. Introduction

It is widely accepted that a pilot in a cockpit is one of the earliest and most enduring symbols of what we can achieve with a positive and collaborative working relationship between humans and machines. If we make a step further and consider as the unit of analysis the airplane, i.e., the pilots plus flight control and automation, as the flight Joint Cognitive System (JCS) then the Air Traffic Management (ATM) is the environment. It is also possible to go one more step further and consider the pilots and the ATM as one system—the traffic flow JCS (Figure 1)— in which case the environment is the airlines, the airports, and the other aviation stakeholders (Hollnagel, 2007). In the era of digitalization and Big Data, this traffic flow JCS faces important and potentially disruptive challenges with the introduction of Artificial Intelligence (AI) both in the air and the in the ground components. It is ubiquitous that the use of AI is spreading rapidly in every industry with aviation and ATM making no exception.

Figure 1. The traffic flow Joint Cognitive System (Hollnagel, 2007).

However, innovative technologies not only provide capacity enhancement opportunities and other performance improvements but also raise new regulatory, safety, cognitive and operational challenges, and tradeoffs. Therefore, there is an urgent need to examine the introduction of AI cautiously. In this paper, we present an initial attempt to detect and document the fundamental challenges of implementing AI, in the European ATM system through the lens of Cognitive Systems Engineering (CSE) paradigm (Hollnagel and Woods, 2005; Woods and Hollnagel, 2006).

1.1 Motivation

The significant and continued growth in air traffic in the years prior to the COVID-19 pandemic has prompted considerable exploration of the use of AI in the ATM. It is expected that AI will provide the additional capacity to meet the challenges of increasing air traffic complexity due to sustained growth and new airspace users and support more efficient and environmentally friendly operations while maintaining and increasing current safety levels. Modern ATM systems comprise many airspace sectors with varying air traffic flows that interact in complex ways and evolve dynamically. ATM is a work domain that relies on the cognitive functions of Air Traffic Controllers (ATCOs) and their collaboration with flight crews, airport operators, network managers and the other aviation stakeholders to control the airspace, manage safety and adapt to the changing demands of new technological initiatives (Kontogiannis and Malakis, 2017). According to EASA (2021) while the concept of AI has been in existence since the 1950s, its development has significantly accelerated in the last decade due to three factors: a) significant improvements in the capacity to collect and store massive amounts of data (i.e., Big Data), b) significant increases in computing power and c) development of powerful algorithms and methods. From a purely AI view the ATM system is a real-time safety- critical decision-making process in highly dynamic and stochastic environments where human air traffic controllers monitor and direct many aircraft flying through its designated airspace sectors (Brittain et al, 2020). The extensive introduction of AI is expected to create a new ATM environment which will be tightly coupled, more complex to cope, with increased air traffic, and pressing needs for a) minimization of delays, b) accommodating a diverse array of autonomous aircraft, c) operating in adverse weather, d) smoothing out 4D aircraft trajectories and e) minimizing environmental impact. AI is expected to increase the resilience and the flexibility of the system (i.e., increase support during emergency in flight or on the ground or unusual situations, as severe weather, failures etc.) and to increase the situational awareness of the operators (e.g., ATCOs and Pilots). In parallel ongoing EASA’s projects such as Extended Minimum Crew Operations (eMCOs) and Single Pilot Operations (SiPOs) rely heavily on AI and the application of powerful Machine Learning (ML) methods. eMCOs are defined as operations where the flight time is extended by means of rest in flight with the minimum flight crew. It is achieved by allowing operations with one pilot at the controls, during the cruise flight phase; however, offering an equivalent overall level of safety through compensation means (e.g., ground assistance, advanced cockpit design with workload alleviation means, pilot incapacitation detection). SiPOs are defined as end-to-end single-pilot operations. Air operations regulation already foresees conditions and limitations under which these types of operations are allowed. In the future, it is expected that these conditions and limitations will need to evolve to extend single-pilot operations to large airplanes, provided that compensation means (e.g., ground assistance, advanced cockpit design with workload alleviation means, capability to cope with pilot incapacitation) are in place in order to provide for an overall level of safety equivalent to today’s two-pilot operations. EASA is working with interested industry stakeholders to explore the feasibility of such operational concepts, while maintaining current safety levels. It is evident that both projects that rely heavily on AI and ML will need ATM support and therefore introduce new operational requirements for the ATC. All these will impose a brand-new array of challenges to the ATM systems in the next 10 to 15 years. In this context we attempted to elicit and document fundamental challenges to the ATM system from the introduction of AI with of the view of drawing the attention to the potential side effects that we must act upon promptly.


2. Methodology

We used a range of methods over several phases of fieldwork, documentation analysis and finally divergent thinking, comparative reasoning, and integrative thinking to compile the final list.

In the fieldwork phase, we used many methods. These ranged from participation in structured group discussions (e.g., concerning AI certification) and in-depth discussions (e.g., AI related projects). From a Cognitive Systems Engineering (CSE) perspective, these techniques belong to the ‘natural history’ family of methods that are based on a diverse collection of observations in situ (Hoffman and Militello, 2009). This was a recurrent step that was used throughout the whole process. The result of each round was an improved version of the list of fundamental challenges. The next step was to consolidate the list by performing a documentation analysis. During this step we applied a documentation analysis of the most recent reports, white papers, position papers and technical documents from ATM and aviation organizations (EASA, 2020, 2021; EUROCONTROL, 2021a, 2021b; CANSO, 2021, 2021) regarding digitalization and AI in the European continent. The next step was to perform a literature review. There is an extensive body of high-quality research in the human factors and CSE literature that can inform the development and application of automated systems which is beyond the scope of this paper. Therefore, we decided to concentrate on some influential research publications and reports in the areas of automation AI, CSE and ATM (Bainbridge, 1983; Parasuraman and Riley,1997; Dekker and Woods, 1999; Moray and Inagaki, 1999; Parasuraman et al. 2000; Russell et al 2010, Woods and Sarter, 2000; Woods and Branlat, 2010; Woods et al, 2010; Norman, 2013). We also reviewed similar research efforts (Prevot et al. 2012), acceptance of automation studies (Westin, et al. 2016) and introduction of ML techniques can be used in developing classification rules and eliciting knowledge in the area ATM system (Malakis et al. 2020). Finally, we applied divergent thinking, comparative reasoning, operational expertise, and integrative thinking by capitalizing on the knowledge and operational expertise of the team members through successive rounds of drafting, commenting, and finalizing the list.

In compiling the final list of fundamental challenges, we followed the next principles:

Fundamental challenges should not be overlapping. To this end we performed a vertical division into five widely accepted levels in the ATM system, which were identified through the literature review. These levels are: 1) Political / Regulatory. 2) Air Navigation Service Providers (ANSPs). / Business. 3) Technical. 4) Operational. 5) ATCOs.

Fundamental challenges should not be contradictory. This is a critical requirement as a challenge in one level may be neutral or even beneficial in another especially between Political / Regulatory which is the highest level of abstraction and all the other levels.

Independent of the size, complexity, staffing levels and nature of operations of the ANSPs to which they apply. It is widely accepted that the ATM provision in the European Contingent is fragmented (Finger et al., 2014; Andribet et al., 2022). Therefore, compiling the list having in mind only one or two ANSPs will certainly distort and bias the analysis. To avoid this, we selected only those challenges that are common for the majority of the European ANSPs.

Address the European ATM although most of them can be applied to other ATM systems worldwide. European ATM is a unique blend of increased traffic levels, capacity shortfalls, ANSPs fragmentation and complexity among all the worldwide ATM systems. The US ATM system for comparison has an extensive degree of cohesion in terms of ATM provision, CNS systems used, training of ATCOs and centralized oversight. Therefore, we focused only on the European ATM system although quite a few of the challenges may also be valid in other ATM systems worldwide subject to more research.

Pragmatic in nature. Given the diversity of AI related projects there is always the risk that superficial, inconsequential, close to maturity or even remote challenges to be identified. To avoid this trap, we focused only on pragmatic challenges that were uniformly identified and well understood in nature.

Not connected to a particular AI paradigm and ML method to the extent possible. There is plethora of methods, tools, paradigms, and application with regard of AI and ML (Russell et al 2010, Barredo Arrieta, 2020). Focusing on a particular method, tool or application would have resulted in a narrow and potentially outdated analysis. For instance, there is a growing literature on the explainability issues of AI (Barredo Arrieta, 2020). Especially for Neural Networks which is the state of the art in Deep Learning. However, focusing only Neural Networks would have restricted our scope into a particular paradigm and method.


3. Results

The fundamental challenges from the introduction of AI in the European ATM system are outlined below.

3.1 Political / Regulatory

The challenges in the Political / Regulatory area are the following:

  • Fragmentation of the European ATM sector (Finger et al., 2014; Andribet et al., 2022).
  • Complexity and novelty of AI related products certification.
  • Obsolescence of traditional development assurance frameworks that are not adapted to ML and development of the Learning Assurance Concept (EASA, 2021).
  • State sovereignty concerns.
  • Legal differentiation between Air Traffic Services Providers and ATM Data Service Providers.
  • Geographical redundancy and availability of ATM data centers.
  • Complexity of agreements on risk sharing and charging mechanism between States.
  • Definition, sharing, and applying a common policy on ethics, related to AI.
  • Maintain the credibility and reliability of the ATM systems by promoting social acceptability and change management.
  • Build the trust of operators through a system of rules that distribute and clearly define the responsibilities and operating limits of the AI and the operator.

3.2 ANSP / Business

The challenges in the ANSP / Business area are the following:

  • Organizational transformation issues.
  • Insurance and liabilities.
  • Constraints from the early adoption of new technology.
  • Unclear cost-effectiveness benefits.
  • Complexity of service borders and scope definition.
  • Costly reinforced cybersecurity infrastructure.
  • Complexity of service continuity requirements.
  • Disruption of established knowledge sharing, learning procedures, and practices within ANSPs.
  • Encourage simulation, training, and feedback paths from operators, to be shared with systems developers.
  • Build a new change management policy to gradually drive operators through new technologies and working methods.

3.3 Technical

The challenges in the technical area are the following:

  • Complexity of sharing of AI infrastructure between countries.
  • Extreme dependency of the ML models on the datasets that are used for training, validation, and testing.
  • Curse of dimensionality, which refers to the extraordinarily rapid growth of the complexity, as the number of variables (or dimensions) increases (Bellman, 1957).
  • Significant differentiation of AI solutions for Tower, Approach and Area Control operations.
  • Tailor made AI solutions for each use case that cannot be easily generalize across same type of units in different geographical areas.
  • Consolidation of critical data to guarantee consistency, integrity, and safety of displayed information on the Controllers’ Working Positions.
  • Develop backup systems to face failures or unusual situations in complex AI systems.
  • Define effective AI methods to detect, frame into context, and interpret into decisions weak signals.

3.4 Operational

The challenges in this area are the following:

  • A scale shift in complexity in terms of the density of interdependencies across processes and activities.
  • Synchronization of operational procedures between Air Traffic Services Units (ATSUs) and between ATSUs and Network Manager.
  • Incompatibility with existing Concept of Operations (CONOPS) and the need of development of new ones.
  • Explainability issues of the ML models.
  • Function allocation issues. Avoiding leftover strategy where the ATCOs are expected to assume control when automation fails.
  • Develop, validate and harmonize the integration of AI technologies in the whole system, among all users (ATCOs, pilots, aerodrome operators and Network Manager).
  • Disruption of established relationships, lines of communication and the ability to exert authority.

3.5 ATCOs

The challenges in this area are the following:

  • Keeping the ATCO ‘in the loop’ and situationally aware and able to intervene.
  • Disruption of established patterns in coordinated activity between ATCOs’ and between ATCOs’ and flight crews.
  • Disruption of established patterns of resilience.
  • Increasing instances of automation surprises and clumsiness.
  • Increased space for potential and new type of errors that cannot be easily foreseen.
  • Demands for new kind of knowledge and skills.
  • Demands for more threads to track that makes attention management difficult.
  • Demand for the development of new mental models, how the AI system works, how it fails, why it fails, and how to adapt (Borders et al, 2019).
  • ATCOs acceptance of their new roles and remits.
  • Synchronizing an increased number of micro-cultures of ATCOs’ communities which are based on their local practices and local affordances.
  • De-Skilling of ATCOs.
  • ATCOs’ intervening during failures and contingencies, (Leftover strategies).
  • Vigilance / boredom tradeoffs.
  • Managing of social aspects (e.g., relocation and mobility of ATCOs).
  • Resistance from ATCOs’ who could legitimately fear for their jobs.

4. Discussion

Although the pandemic of Covid-19 significantly decreased the number of flights by slowing down the air transportation system in 2020 and 2021, this is not going to be a permanent situation. Commercial aviation already (June 2022) shows signs of a significant rebounce. The ATM systems is operating on its usual high traffic levels and the need from the introduction of AI in the ATM will gain momentum. AI is promising costs reduction, flight efficiency, improved strategic planning, enhanced trajectory prediction, and fuel efficiency to name but a few. However, it is evident from the challenges above that many and difficult to tackle challenges emerge. The range of challenges that were identified previously may well be the enablers of following safety and performance related patterns.

4.1 Difficult organizational and operational trade-offs

Operations rooms are hectic workplaces, and in many cases, work demands exceed resources, so ATCOs have to do their best and manage their traffic by adjusting their practices to meet existing conditions. In this sense, they are trying to maintain a continuous balance between demands and resources. Moreover, this does not only concern high workload situations, but also low workload periods, during which ATCOs need to maintain their performance and awareness levels despite not having enough to do to keep them engaged. The effort to tailor human performance to work demands can be described as if it involved a trade-off between efficiency and thoroughness. This view has received particular emphasis from Hollnagel’s (2009) proposition of the Efficiency-Thoroughness Trade-Off (ETTO) principle. To cater for efficiency, ATCOs generally try to achieve their goals by keeping their efforts and resources (for example workload) as low as possible. Safety, on the other hand, requires that more resources are spent on ensuring that the necessary conditions are in place, so that performance goals are achieved without risks. Safety implies that ATCOs spent more time in thinking whether preconditions for an activity are met, execution conditions are right and preparations for contingencies are made in advance. ATCOs have to reach both their safety and performance goals, neither of which should be achieved at the expense of others. In this line of reasoning unresolved challenges from AI projects will most certainly disrupt this fragile balance and create room for new and more complex dilemmas. Additionally, very thorough investigation will be required to determine how many, and which ATCO tasks will AI take over and/or assist with. For example, completing the simple and routine tasks would be beneficial in a high workload situation, as this would allow ATCOs to focus on the complex tasks, but detrimental in a low workload situation, as it would even furthermore reduce ATCOs engagement and could lead to reduced situational awareness and performance levels.

4.2 Difficult organizational and operational trade-offs

Current safety methodologies cannot cope well with AI related projects especially when it comes to learning assurance (EASA, 2020, 2021). EASA developed a multiyear project to address exactly this type of weakness in certification. ANSPs’ safety methodologies focus on the needs of single agents, but do not allow risks of different types and sources to be assessed with reference to each other, singly or in combination in dynamic environments. Hence, dysfunctional interactions arise that are beyond the control of single agents and current methodologies cannot adequately deal with them.

4.3 Transform or transfer hazards to other stakeholders

In many cases, risks may be transformed or transferred among ATM stakeholders since the solution of one’s own concerns may create problems elsewhere. For instance, adverse weather is a safety hazard for all flight operations. When weather cells are encountered, flight crews may request to circumnavigate cells which could increase traffic complexity for ATCOs particularly in congested airspaces. Hence, granting a cell circumvention to aircrews may increase the risk of separation minimum infringement and reduce the margin of maneuver for controllers. On the contrary, flight operation hazards are effectively reduced. The increase in air traffic complexity (e.g., more conflicts points) as several air crews are requiring changes to their routes to circumvent the weather cell cannot always be mitigated by ATFCM restrictions alone. Corver and Grote (2016) showed that area controllers, in addition to reducing and acknowledging uncertainty, may deliberately increase uncertainty to increase flexibility for other actors in the system to meet their operational goals. On severe weather days, ATCOs often forgo a few procedures, e.g., coordination requirements, to increase their overall efficiency in handling the complex traffic and to ensure the safety of the aircraft. These results are particularly important as uncertainty is likely to increase in future AI supported operations of area control ATC especially since explainability issues will remain an issue (Barredo Arrieta, 2020). Therefore, risk transferability between ATM stakeholders which is common now and, in a sense, understood (because work practices are built upon it) will most probably be disrupted with unwanted safety and performance consequences. For this reason, any AI that is introduced in ATC has to be modeled in a way that matches the ATCO working practice and culture.

4.4 Patterns of events that are difficult to anticipate, monitor or comprehend

Complex aviation systems require pilots and controllers to anticipate critical events and stay ahead of traffic so that they get prepared for new evolving situations. For this reason, the set capacity values for a specific sector are often lower than the actual capacity. In fact, many automated support tools have been designed to allow practitioners to foresee the evolution of weather and traffic, which then gives information on whether a sector would become overloaded and whether the gap between set capacity value and actual capacity will be filled. Anticipation of evolving traffic becomes very important since traffic patterns may be affected by factors controlled by different ATM stakeholders (e.g., airlines, airports and ANSPs). With the introduction of AI in the air and on the ground new patterns of events that are difficult to anticipate, monitor or comprehend will materialize. In addition to preparing and updating the currently existing contingency plans, consideration will also have to be made towards potential AI-related issues. This will certainly entail implications for the safety, performance, and capacity of the ATM system.


5. Conclusion

There are a lot of questions to be answered in those five levels we identified, with the most critical being at the ATCOs level. In this context IFATCA has created the concept of Joint Cognitive Human Machine System (JCHMS) or a Human Centric approach and wishes to influence ICAO and standardization bodies such as EUROCAE and EASA in Europe as well as other global organization such as RTCA, SAE in the way these new technologies are designed. This is a first step towards this goal of raising awareness to challenged raised by the introduction of AI in the European ATM system. It is widely accepted that each technology shift—as the wide scale introduction of AI in the ATM —extends the range of potential control. In doing so it also extends the range of potential control of the JCS that performs the work as well. Woods and Branlat (2010) framed these challenges in the form of two simple questions: What does it now mean to be ‘in control’? How to amplify control within the new range of possibilities. We argue that the way forward in answering the challenges that we have documented above includes the provision of definitive answers to these questions.


6. Directions for Future Research

The findings of this study are pending further validation and generalization due to the exploratory character of the research. However, many of the challenges drawn from this study are expected to remain relatively stable when additional studies are carried out. Errors introduced by designers in developing AI systems can themselves be major source of safety and operational problems in the ATM. In our view a) many studies must be designed that take into account the challenges we identified and b) aircraft operations related projects such as eMCOs and SiPOs ought to include the prospect of collaborating with an AI assisted ATM system.


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Annex III – Single European Sky and Resilience in ATM – Can this be a “Win-Win” for the Aviation Industry? The IFATCA Input

(IFATCA Joint Cognitive Human Machine Systems Team)

Introduction

Resilience is defined as the ability to succeed under varying conditions, so that the number of intended and acceptable outcomes is as high as possible (Hollnagel, 2018). In the context of resilience, Sustained adaptability refers to the ability to continue to adapt to changing environments, stakeholders, demands, contexts, and constraints (in effect, to adapt how the system in question adapts) (Woods, 2018). The EU’s Single European Sky (SES) legislation has introduced a Performance Scheme which defines mandatory performance targets for the Air Navigation Service Providers (ANSPs) of EU Member States. The Performance Review Body (PRB) advises and supports the Commission in setting up binding performance targets and thereby, in a way, acts as the regulator for ATM at the European level. In this sense, in the European ATM system, resilience and sustained adaptability are considered to be defined by the scope of the SES Performance Scheme.

The Covid-19 crisis demonstrated that forecasting and charging scheme deficiencies can produce results that provide a false reassurance about the degree of financial stability of the ANSPs. Undoubtedly, the SES has a large share of responsibility in institutionalizing the financial and performance fragility of the European ANSPs. Furthermore, by considering the unanticipated synergy between the effects of the pandemic of Covid-19 and the continuing war in Ukraine we argue that volatility in air traffic and consequently in financial results may be the norm and not the exception anymore.

For the ATM system to sustain resilient performance it needs the capabilities to respond to varying conditions typically through adaptation and coordination and to be able to sustain this adaptability. Traditionally, ANSPs, and the wider context of ICAO, EASA, EUROCONTROL, local CAAs and many more have built systems with resilient performance that have rarely led to fatal accidents. PRC (2022) and EASA (2022) argue that historical evidence shows that overall safety levels in the EUROCONTROL and EASA member states area are high, however there is no room for complacency. Whereas in terms of safety we are performing well, the ATM system has had bigger problems when it comes to efficiency. The SES is the ‘dark’ side of resilience and has reduced the ANSPs ability to adapt to disturbances and crisis. Focusing on the SES’s influence on the ATM systems ability to adjust it functions, configurations and sources of adaptive capacity mainly operational capacity, to the changes that have influenced the system for last two decades IFATCA contends that:

1. the performance scheme has been proven to reduce the ANSPs ability to maintain resilient performance because:

a) When faced with a crisis e.g., financial crisis, political upheaval, or a pandemic as Covid-19 the performance scheme leads to operational capacity deficiencies.

b) The performance scheme leads to short term fixes rather than sustaining long term resilient performance.

c) The Reference Period (RP) 3 performance scheme lasting only 5 years, needed to be redesigned when confronted with a crisis.

2. the charging system has reduced the ANSPs ability to sustain resilient performance:

a) Because the airlines are paying for the ATM system, there is a tendency to react to airline problems by putting pressure on the ATM system instead of looking at the pragmatic needs of the ATM system.

b) The ATM system, being a critical transportation infrastructure, needs to run 24/7 no matter the traffic demand.

c) Essentially, all ANSPs went bankrupt in 2020/2021, when there were no aircraft flying in the air.

3. That the philosophy of Single European Sky ATM Research (SESAR) and SES has reduced the ANSPs ability to maintain resilient performance.

a) The SESAR philosophy cause brittleness through reducing adaptive capacity.

As a viable way to improve the SES, we suggest investing time and resources in reframing the SES Environment performance indicator to embrace the interdependencies between: Safety, Capacity and Cost Efficiency. Such an indicator needs to be a mix of future expected capacity (a minimum of 10 years), a pragmatic expectation of the promise of technological innovations and how to build in system margins, both financial and staff, to cope with upcoming crises. With such an indicator it could be possible to change the short-term financial focus of the performance scheme to focus on the environment and ANSPs margins to cope with the actual fluctuation of traffic. In a fragile operational and financial context, we also argue that there is an urgent need for a pragmatic definition of a unified framework of resilient performance that includes the joint cognitive human machine ensemble, organizational, financial, and environmental perspectives. To realize this objective, in this paper, we present an initial description of a proposed framework of pragmatic solutions based on the tenets and philosophy of resilience engineering.


Resilient performance is the key to future ATM development

Crisis preparedness, response and recovery remain amongst the most challenging problems facing organizations because of their (a) large social and economic costs in their management and (b) limited organizational learning from previous crisis. Much of the history of the Covid- 19 crisis in the aviation domain can be interpreted broadly as an underestimation of risks, not only of the probability of Black Swans category events – events that are unpredictable, result in severe and widespread consequences and after their occurrence, people rationalize the event as having been predictable (Taleb, 2009) – but also, of the distinctive ANSPs’ financial fragility to them. The Covid-19 crisis demonstrated that forecasting and charging schemes based on poorly designed traffic growth scenarios, focus on short time-period contingency scenarios, omitted shocks, excessive reliance on flawed financial and economic models, inappropriate methods and narrow coverage can produce results that provide a false reassurance about the degree of the financial stability of the ANSPs in a crisis like the Covid-19 pandemic. Undeniably SES has promoted these deficiencies with the net result of institutionalizing financial and performance fragility to the European ANSPs.

The resilience engineering perspective looks into the capabilities of the system to cope with increasing demands and surprise events – both fundamental and situational and compensate by adapting its performance. Consequently, the focus shifts from individuals to teams and organizations who have to cope with multiple goals, organizational pressures, uncertainty and complexity. Resilience engineering is about understanding and anticipating what sustains and what erodes adaptive capacity. The definition implies four potentials of resilience, each representing an essential system capability (Hollnagel, 2018). The four essential capabilities are:

  1. Knowing what to do: That is, how to respond to regular and irregular disruptions by adjusting normal functioning.
  2. Knowing what to look for: That is, how to monitor events and actions that could become threats in the near term as well as monitor one’s own performance.
  3. Knowing what to expect: That is, how to anticipate developments and threats further into the future, such as potential disruptions, pressures, and consequences.
  4. Knowing what has happened: That is, the ability to learn the right lessons from experience.

We argue that the Covid-19 crisis exposed that all three of the essential potentials were flawed and now it is time to not let the fourth one fail as well. Drawing on the fourth potential we argue that there is strong need for the re-introduction of the appropriate ability to adapt within the system so that it supports more resilient performance operations-wise and financial-wise after the Covid-19 crisis.

In the Resilience Engineering paradigm Woods and Branlat (2010, 2011) proposed three patterns of adaptive breakdown which represent patterns of maladaptive behaviour:

  1. Complexities in time – Decompensation: This pattern corresponds to an escalation of demands while the system is not capable of adapting and acting fast enough upon the set of disturbances. Under such circumstances, components are stretched to their performance limits and the system’s overall control of the situation collapses abruptly.
  2. Complexities over scales – Working at cross-purposes. System’s behaviour, which exist in the context of networks of interdependencies (functional, structural, temporal) and cross-scale interactions, have implications at a larger scale than simply at the level of elements producing the behaviors.
  3. Complexities in learning – Getting stuck in outdated behaviours. This pattern emerges when a system gets stuck in implementing behaviors that were successful in the past and fails to recognize that the conditions for their implementation are no longer met. The pattern relates to breakdowns in how systems learn, either from past experience or dynamically, as situations unfold.

It is evident, in the authors view, that Covid-19 crisis uncovered all three patterns of adaptive breakdown in the European ATM system. In this line of reasoning, we propose a framework of possible solutions to counteract both financial and performance fragility:

  1. Increased Personnel Redundancy: Hiring and training of new Air Traffic Controllers. Normalizing lean hiring and rostering personnel strategies is a certain path to performance deficiencies. A large share of delays this summer and the last 15 years is directly attributed to ATC staffing (PRC, 2022). This is a textbook example of exhausting capacity to adapt as disturbances/challenges cascade (Decompensation).
  2. Introduction of Stress Tests. A stress test, in financial terminology, is an analysis or simulation designed to determine the ability of a given institution to deal with an economic crisis. Economic and financial models have limitations and constraints, including misspecification, estimation using assumed and sometimes inaccurate probability distributions, etc. Therefore, using such models to estimate the potential impact of shocks may lead to increasingly inaccurate estimates. Instead of doing financial projection on a “best estimate” basis, an ANSP and its regulators may do stress testing where they look at how robust an organization is in certain crashes. Useful performance indicators can emerge if these stress tests are properly designed and executed. Exploring the utilization of other criteria for the charging scheme than the ones based solely on traffic numbers may emerge as a consequence of these stress test. This is a textbook example of the world changes but the system remains stuck in what were previously adaptive strategies (Getting stuck in outdated behaviors).
  3. Moving Away from Uninformative Safety, Financial and Performance Indicators. Current performance indicators belong to the ‘lagging category’ with minimal predictive value that may mislead from the complexities of the actual operational context. For instance, measuring an ANSP performance in terms of delay minutes accumulated is incorrect and highly misleading. It could have been a very busy day with adverse weather, lots of CBs (Cumulonimbus cells) and circumnavigation and controllers performing above standard to ensure separation and yet – delay minutes indicate low performance levels. A statistical analysis of performance-related information may be indicative of emerging threats from many sources. However, a resilient performance intelligence function requires a combination of reactive, proactive, and predictive indicators that will detect emerging threat patterns. The information published by PRC can only be used to assess how frequently organizations come close to the performance, financial and safety margins. Performance indicators are complemented with trends in the last decades and some elementary descriptive statistics. Therefore, stakeholders cannot discern anything more that some well- established but highly uninformative trends (Getting stuck in outdated behaviors).
  4. Restructuring of the Air Traffic Flow and Capacity Management (ATFCM) system. ATFCM systems needs to move towards a more command and control system and consider rectifying the inefficiencies of the Computer Assisted Slot Allocation (CASA) algorithms. Slot allocation algorithms rely mostly on physical factors (e.g., prevailing winds estimated many hours earlier than the actual flight) but fail to account of actual aircraft performance, flight profile preferences by airlines and direct routings given by controllers in the en-route phase of flight. Hence, some ATC units may be stretched above their capacity or others may be underutilized because of under- specifications in the ATFCM algorithms. PRC (2022) acknowledged these deficiencies and provided some remedies. This is a textbook example of behavior that is locally adaptive, but globally maladaptive as it to coordinate different groups at different echelons as goals conflict (Working at cross-purposes).
  5. Cultivating Adaptive Diversity. In the European ATM there are several micro- cultures of controllers. These are controllers’ communities exercising and promoting operations based on their local practices and local affordances. For instance, in a major ACC there are 3 different sector groups, and each group has a different working style/culture. These differences happen because the sector size, crossing points, traffic flows and hence needs vary. An adaptive way of working would increase the overall resilience. Institutionalizing a know-your-neighboring-units-work-practices awareness program would be beneficial to ATCOs during their basic or rating training with (familiarization visits to various ANSPs). Getting to know the working styles of your neighbors could help the ATCO understand them better, thus easier to adapt (Working at cross-purposes).
  6. Tap local knowledge. Controllers at the sharp end of each unit know best the causes of safety and performance deficiencies. They rely on “localized performance and safety intelligence” at the individual and team level and respond accordingly. Likewise, parent ANSPs organizations may need to drive more of their sense making to local levels and exploit these more these localized performance and safety intelligence to detect and respond to weak signals of performance and safety deficiencies (Decompensation).

Environmental performance as the overarching SES target

The SES political initiative was introduced in 2004 (Finger and Baumgartner, 2014). In 2011 the SES performance scheme was adopted and is moving toward the end of the third Reporting Period. Currently RP4 is being drafted by the Commission and EASA amongst other agencies. Following the impact of Covid-19 travel restrictions, the SES performance scheme needed to be adjusted in order to cope for future crises. The European Union has adopted its Fit for 55 agenda (that is achieving climate neutrality by 2050) which shall provide the political impetus to meet the required contribution to the decarbonization of all the human activities including aviation. In IFATCA’s view this is a unique opportunity to adjust the ATM sector’s performance to the environmental challenges. This can be achieved by establishing an RP4 performance scheme which puts the environmental performance as the interdependency performance target which is currently lacking in the performance scheme.

Various studies (PRC, 2021; 2022) have highlighted the potential benefit pools of 8-12% ATM can manage as a contribution to the decarbonization of aviation. By creating a systemic approach to manage the ATM contribution to decarbonization IFATCA claims that this could be a win- win situation. To achieve such a win-win situation a systemic approach to managing the European Network needs to be created. This will provide a network- centric approach and the ATM sector under the central coordination of the NM, could start to work to a commonly agreed and shared vision (IFATCA, 2007) of SES. In order to achieve such a network centric approach several currently established operational and institutional set-ups will have to evolve and include an improved virtualized infrastructure. Synchronization and Orchestration needs to be organized (IFATCA, 2007).

SESAR 3 should focus on delivering such a netcentric approach by focusing on the needs of a common virtual Flight Data Processing Systems (FDPS) for the whole Network Management area and provide the ANSP planning tools which assist them to create the needed capacity when required. As the proposed change will have an impact on safety, the safety Key Performance Areas will need to focus on resilient performance while maintaining or extending the current level of safety. New technology will have to be introduced to ATM with a focus on supporting and sustain resilient performance and in particular focus on the principles of Human Machine Teaming. The ATM system can be defined as a “Joint Cognitive System” of people, teams, and artifacts that adapts to the challenges and demands posed by familiar and unfamiliar situations in a dynamically evolving operational context. In this context IFATCA has created the concept of Joint Cognitive Human Machine System (JCHMS) and wishes to influence ICAO and standardization bodies such as EUROCAE and EASA by researching and publishing (IFATCA JCHMS Group, 2022a; 2022b).

With benefits from aircraft technology and Sustainable Aviation Fuels (SAF) only taking real effect beyond 2030, ATM can help reduce emissions by addressing operational inefficiencies in the ATM system in the short to medium term. For every ton of fuel saved, an equivalent amount of 3.15t of CO2 can be avoided. In political discussions, ATM is frequently mentioned to be able to improve fuel efficiency by 10% or more. In reality, it is often not clear what measures are involved and how the results need to be interpreted. There are many different studies aimed at quantifying fuel and flight efficiency. While those studies provide useful and valuable insights, the differences in scope and methodologies make direct comparisons often difficult if not impossible. Previous PRC work (PRC, 2019) has estimated that the benefit pool that can be influenced by ANS is approximately 6-8% of the total gate-to-gate fuel burn (emissions) in the ECAC area. However, most studies apply similar methodologies which compute efficiency gains compared to a theoretical reference which in reality cannot be achieved at system level.

There is clearly scope for further improvement in ANS resilient performance (organizational, system and network performance in financial and business terms). However, it is important to stress that the often-quoted benefit pools cannot be fully recovered, nor can the inefficiencies be entirely attributed to ANS. Full efficiency as envisaged is impossible due to technical and safety aspects (separation minima, adverse weather, avoidance of ‘Danger Areas’ and temporarily segregated areas) or tactical decisions (trade-offs). In fact, environmental objectives for ANS can even be conflicting; for example, noise abatement procedures at airports might lead to longer trajectories and hence additional emissions.

ANS performance can help reducing the environmental impact of aviation which can be broadly divided into the impact on (i) global climate, (ii) local air quality (LAQ), and (iii) noise. Generally, the management of noise is considered to be a local issue which is best addressed through local airport-specific agreements developed in coordination and cooperation with all relevant parties including ANS. Due to the complexity of those local agreements, there are presently no commonly agreed Europe-wide indicators specifically addressing ANS performance in the noise context.

Apart from the active support in noise management decisions, the areas where ANS can contribute to the reduction of aircraft noise are mainly related to operational procedures. Continuous climb (CCO) and descent operations (CDO), noise preferential routes and runways are all in the ANS portfolio and help to avoid unnecessary exposure to aircraft noise. The ATM- related impact on climate is closely linked to operational performance (fuel efficiency) which is largely driven by inefficiencies in the flight trajectory and associated fuel burn (and emissions). Hence, the focus has been traditionally on the monitoring of ANS-related operational efficiency by flight phase which served as a proxy for environmental performance since the distance or time saved by operational measures can be converted into estimated fuel and CO2 savings. Using the theoretical upper ceiling, the ANS contribution to reduce emissions is limited to some 0.3-0.4% of the total CO2 emissions in Europe (SAF ≈ 3.8%).


So, what can ATM do to help?

Increased operational efficiency leads to increased fuel efficiency, a subsequent reduction in emissions and network benefits. Ground infrastructure as a contributor to the Greening of Operations through efficiency improvement. ICAO, with the support of its Committee on Aviation Environmental Protection (CAEP), actively pursues its technical work on measures to reduce the environmental effects of aviation. As it is stated in ICAO document 10013 (2014):

“significant fuel and emissions savings can be realized by an efficient ATM system. New and established technologies and concepts of operations in CNS can provide opportunities to improve the efficiency of ATM. CNS/ATM can permit more direct routings and the use of more efficient flight conditions such as optimum altitude and speed.”

Furthermore, in the same document it is stated:

“New and established technologies and concepts of operations in CNS, such as data link communications, PBN, ADS, FUA and A-CDM can provide opportunities to improve the efficiency of ATM”.

However, degraded, or low Availability and Continuity of CNS Systems and services (e.g., at airports) can lead to alternative routes flown thus more fuel burn and lower capacity or even total lack of service delivery. So, a new study of a new concept that analyzes the relation between CNS outages or systems unavailability with the impact on the environment and safety issues could help to arrive in the future at the elaboration of new useful metrics or KPIs as requested by Strategic Research and Innovation Agenda (SRIA).

When balancing the requirements of safety, efficiency, capacity and the environment, the level of safety shall always be maintained or improved at all stages of the ATM system (operation, maintenance, and development). In other words, respect for the environment dimension should not undermine or respect for safety. In case environmentally driven procedures are introduced in the ATM System, these must take into consideration the increased complexity for the front- end users, namely controllers and pilots. A trade-off between environment and capacity must be considered as part of this management of complexity, as safety is paramount. Any environmentally driven procedure shall not expose the ATCOs and Pilots to undue liability issues.

Individual environmental aspects shall be considered by an ATM environmental management system and documented in an ATM environment case as part of an overall performance case.

Provisions for an ATM environment management system should comprise at least the following requirements:

  • Ensure that the level of safety shall be maintained or improved when environmentally driven procedures are introduced.
  • Ensure that all individual environmental factors are identified and considered while establishing procedures.
  • The actual values (noise levels, fuel consumption and the level of emissions) of the various individual environmental contributors of new or existing procedures should be established in detail for transparency purpose.
  • The interrelation of the various individual environmental factors should be identified and addressed.
  • Provisions for an environment case should comprise at least the following requirements:
    • An environment case is a documented body of evidence that provides argument that a certain procedure is optimized for all individual environmental factors as prioritized by the appropriate authorities.
    • An environment case should provide a detailed overview to the appropriate authorities for the determination of priorities of the individual environmental factors on a strategic level.

Program and measures of emission reductions should take into account:

  • Safe production has the highest priority and might require additional resources.
  • Balancing of emission reduction with competing factors (e.g., noise reduction) needsto be finalized at organizational (strategical level) level and be transparent. However, adjustments need to be possible in the tactical phase with the appropriate training of the concerned staff.
  • New procedures and tasks need to be in range of the capability to adapt and system changes need to enhance the assistance of the staff, including the handling of congested situations.
  • Information about emission reduction measures and training of the staff members involved is required to achieve optimal support.
  • SJU is also requested to evaluate the recommendation for the development of a new Metric on CNS Availability and its’ impact on Environment within the context of SRIA.

Conclusions

In this paper we argued that SES has a large share of responsibility in institutionalizing financial and performance fragility to the European ANSPs, we provided a framework of possible solutions to counteract both financial and performance fragility and we stressed the importance of reframing the SES Environment performance indicator to embrace the interdependencies between several performance areas. Thus, by creating a systemic approach to manage the ATM contribution to decarbonization IFATCA claims that this could be a win- win situation. The research and development of the SESAR projects will have to focus on considering the aspects that we have highlighted so far. Although we are aware of the need to aim for an ATM system that is increasingly attentive to emissions and environmental issues, it is necessary to ensure that these objectives do not conflict with the highest safety standards. Furthermore, it is necessary that any new procedures studied are not going to provide additional burdens to the personnel involved with new tasks that could, in an already particularly congested situation, further aggravate the work of the operating personnel.


References

Baumgartner, M., Finger, M. (2014). The Single European Sky gridlock: A difficult 10-year reform process, Utilities Policy. http://dx.doi.org/10.1016/j.jup.2014.03.004.

EASA, (2022). Annual Safety Review 2021. European Union Aviation Safety Agency.

Hollnagel, E. (2018). Safety-II in Practice: Developing the Resilience Potentials. Routledge. ICAO, (2014).

IFATCA JCHMS Group, (2022a). Reducing the gap between designers and users, Why are aviation practitioners here again? Presented at The International conference on Cognitive Aircraft Systems – ICCAS, Toulouse France, June 2022.

IFATCA JCHMS Group, (2022b). Challenges from the Introduction of Artificial Intelligence in the European Air Traffic Management System. Presented at IFAC/IFIP/IFORS/IEA Symposium on Analysis Design and Evaluation of Human-Machine Systems, San Jose USA, September 2022.

IFATCA, (2007), A statement on the future of Global Air Traffic Management. Montréal, IFATCA.

Operational Opportunities to Reduce Fuel Burn and Emissions. Document 10013. First Edition. Montreal: International Civil Aviation Organization.

PRC, (2020). Performance Review Report 2019. Performance Review Commission. Eurocontrol, Brussels.

PRC, (2021). The role of ANS in a decarbonising world –Current knowledge and questions for debate. Performance Review Commission. Brussels, Eurocontrol.

PRC, (2022). Performance Review Report 2021. Performance Review Commission. Eurocontrol, Brussels.

Taleb, N.N. (2007). The Black Swan: The Impact of the Highly Improbable. Penguin, London.

Woods, D. D., Branlat, M. (2010). Hollnagel’s test: being ‘in control’ of highly interdependent multilayered networked systems. Cognition, Technology and Work 12(2), 95-101.

Woods, D. D., Branlat, M. (2011). Basic patterns in how adaptive systems fail. In E. Hollnagel, J. Pariès, D. D. Woods, & J. Wreathall (Eds.), Resilience Engineering in Practice. Farnham, UK: Ashgate.

Woods, D.D. (2018) The theory of graceful extensibility: basic rules that govern adaptive systems. Environment Systems and Decisions. 38:433–457.

Annex IV – A Framework for Supporting Adaptive Human-AI Teaming in Air
Traffic Control

Malakis Stathis* [0000-0003-2182-4597], Baumgartner Marc*, Berzina Nora*, Laursen Tom*, Smoker Anthony*, Poti Andrea*, Fabris Gabriele*, Velotto Sergio*, Scala Marcello*, Kontogiannis Tom**

* IFATCA Joint Cognitive Human Machine System (JCHMS) group. International Federa- tion of Air Traffic Controllers Associations, Montreal, Canada.

** Cognitive Ergonomics & Industrial Safety Laboratory, Department of Production Engi- neering & Management, Technical University of Crete, Chania Hellas stathis.malakis@gmail.com

Abstract

In recent years, the growth of cognitively complex systems has motivated researchers to study how to improve these systems’ support of human work. At the same time, there is a momentum for introducing Artificial Intelligence (AI) in safety critical domains. The Air Traffic Control (ATC) system is a prime example of a cognitively complex safety critical system where AI applications are expected to support air traffic controllers in performing their tasks. Nevertheless, the design of AI systems that support effectively humans poses significant challenges. Central to these challenges is the choice of the model of how air traffic controllers perform their tasks. AI algorithms are notoriously sensitive to the choice of the models of how the human operators perform their tasks. The design of AI systems should be informed by knowledge of how people think and act in the context of their work environment. In this line of reasoning, the present study has set out to propose a framework of cognitive functions of air traffic controllers that can be used to support effectively adaptive Human – AI teaming. Our aim was to emphasize the “staying in control” element of the ATC. The proposed framework is expected to have meaningful implications in the design and effective operationalization of Human – AI teaming projects at the ATC Operations rooms.

Keywords: Air Traffic Control, Cognitive Systems Engineering, Artificial Intelligence.


1. Introduction

In recent years, there has been a realization that the growth of cognitively complex systems has motivated researchers and practitioners from diverse backgrounds to study how to improve these systems’ support of human work. At the same time, there is a momentum for introducing Artificial Intelligence (AI) in the safety critical domains building upon:

a) Advances in capacity to collect and store massive amounts of data.

b) Significant increases in computing power, and

c) Development of increasingly powerful algorithms and architectures.

The Air Traffic Control (ATC) system is a prime example of a cognitively com-plex safety critical system where AI applications are introduced. Soon AI systems are expected to support even more than now air traffic controllers, flow controllers, operational supervisors, and flight information officers in performing their safety critical tasks within the ATC ecosystem. Furthermore, the widespread introduction of AI is expected to create a new ATC environment, which will be tightly coupled, more complex to manage, and with pressing needs for:

a) Minimization of delays.

b) Accommodating a diverse array of autonomous aircraft.

c) Operating smoothly in adverse weather conditions.

d) Smoothing out 4D aircraft trajectories, and

e) Minimizing environmental impact.

European Union [1, 2] is envisioning a Digital European Sky and an irreversible shift to low and ultimately no-emission mobility. With this goal in mind, ATC and aviation will evolve into an integrated digital ecosystem characterized by distributed data services. This is planned to be accomplished mostly by leveraging digital tech-nologies to transform the aviation sector. The aim is to deliver a fully scalable Air Traffic Management (ATM) system for manned and unmanned aviation that is even safer than today’s, and, based on higher air ground integration. The ATM provides an essential service for aviation. While the essence of ATM is and will always re-main, to ensure the safe and orderly execution of all flights it needs to do so in the most environmentally friendly and cost-efficient way [1].

In this context, International Federation of Air Traffic Controllers (IFATCA) has created the concept of Joint Cognitive Human Machine System (JCHMS) or a Hu-man Centric approach and wishes to influence ICAO and standardization bodies such as EUROCAE and EASA in Europe as well as other global organization such as RTCA, SAE in the way these new technologies are designed. Nevertheless, the de-sign of AI systems that effectively support humans poses significant challenges [3].

In the ATC domain, as early as 1951, Fitts [4] proposed the use of automation to replace human functions with technology. In this approach the capabilities (and limitations) of people and machines are compared on a number of salient dimensions, and function allocation is made to ensure that the respective capabilities are used in an optimal way (Compensatory principle). However, it failed to acknowledge the essential condition that it is necessary at all times to remain in control of what hap-pens. Humans are aware of what they are doing and can imagine what the outcomes may be. Machines and technology can do neither. Digitalization relies on highly effective but poorly understood algorithms. By replacing human functions with technology that is not fully comprehensible, control is gradually and irretrievably lost.

It is acknowledged that although AI has many potential benefits, suffers from a number of challenges for successful performance in complex safety critical real-world environments, including brittleness, perceptual limitations, hidden biases, and lack of a model of causation important for understanding and predicting future events. These limitations mean that AI will remain inadequate for operating on its own in many complex and novel situations for the foreseeable future, and that AI will need to be carefully managed by humans to achieve their desired utility [5].

Central to these challenges is the choice of the model of how air traffic control-lers perform their tasks. AI algorithms are notoriously sensitive to the choice of the models of how the human operators perform their tasks. It is widely accepted that effective sensemaking and decision support systems cannot be designed by an engineer’s intuition alone.

The design of AI systems should be informed by knowledge of how people think and act in the context of their work environment. Undoubtedly, AI design teams must be knowledgeable about computer software and the state of the art in algorithms, but if the AI systems fail to support cognitive functions of air traffic controllers, they will be eventually rejected in the workplace.

In this line of reasoning, the present study has set out to propose a framework of cognitive functions of air traffic controllers that can be used to support effectively adaptive Human – AI teaming. Our aim was to develop a pragmatic framework that will provide a basis for building adaptive human – AI teaming architectures by respecting the human centric nature of the ATC system and emphasizing the “staying in control” element of the ATC. With this goal in mind, we employed Cognitive Systems Engineering (CSE) [6, 7] methods to develop a framework of cognitive functions for air traffic controllers. We have chosen CSE as our research paradigm because CSE has grown out of a need to design systems within which people can interact effectively. Moreover, CSE is an approach to the design of technology, training, and processes intended to manage cognitive complexity in sociotechnical systems [8]. The proposed framework is expected to have meaningful implications in the design and effective operationalization of Human – AI teaming projects at the ATC Operations rooms.


2. Making sense of the “new” ATM ecosystem

A pilot in a cockpit is one of the earliest and most persistent symbols of what we can achieve with a positive, collaborative, and adaptive working relationship between humans and machines. Taking a step further and consider as the unit of analysis the airplane, i.e., the pilots plus flight control and automation, as the flight Joint Cognitive System (JCS) then the ATM is the environment. If we make one more step further and consider the pilots and the ATM as one system—the traffic flow JCS in which case the environment is the airlines, the airports, and the other aviation stakeholders [9].

If we consider the ATM comprised of three layers, then current architecture is characterized by:

  • Airspace layer: Limited capacity, poor scalability, fixed routes, fixed national airspace structures.
  • Air traffic service layer: Limited automation, Low level of information sharing.
  • Physical layer: Fragmented ATM infrastructure. 

Digitalization and especially AI, which lies at its core, is expected to enable [1]:

  • Dynamic & cross FIR airspace configuration & management Free routes High resilience at the Airspace layer.
  • Automation support & virtualization Scalable capacity at the Air traffic service layer.
  • Integrated and rationalized ATM infrastructure at the Physical layer.

It is evident that in the era of digitalization and Big Data, the ATM ecosystem faces important and potentially disruptive challenges with the introduction of AI both in the air and the in the ground. It is ubiquitous that the use of AI is spreading rapidly in every industry with aviation and ATM making no exception.

However, innovative technologies not only provide capacity enhancement opportunities and other performance improvements but also raise new regulatory, safety, cognitive and operational challenges, and trade-offs [3]. Therefore, there is an urgent need to examine the introduction of AI and more importantly the human-AI teaming cautiously.

The significant and continued growth in air traffic in the years prior to the COVID-19 pandemic has prompted considerable exploration of the use of AI in the ATC. It is expected that AI will provide the additional capacity to meet the challenges of increasing air traffic complexity due to sustained growth and new airspace users and support more efficient and environmentally friendly operations while maintaining and increasing current safety levels. Modern ATM systems comprise many airspace sectors with varying air traffic flows that interact in complex ways and evolve dynamically. ATC is a work domain that relies on the cognitive functions of Air Traffic Controllers and their collaboration with flight crews, airport operators, network managers and the other aviation stakeholders to control the airspace, man-age safety and adapt to the changing demands of new technological initiatives [10].

From a purely AI view the ATM system is a real-time safety-critical decision-making process in highly dynamic and stochastic environments where human air traffic controllers monitor and direct many aircraft flying through its designated airspace sectors [11].

AI is expected to increase the resilience and the flexibility of the system (i.e., in-crease support during emergency in flight or on the ground or unusual situations, as severe weather, failures etc.). Furthermore, ongoing projects such as Extended Min-imum Crew Operations (eMCOs) and Single Pilot Operations (SiPOs) rely heavily on AI and the application of powerful Machine Learning (ML) methods. eMCOs are defined as operations where the flight time is extended by means of rest in flight with the minimum flight crew. It is achieved by allowing operations with one pilot at the controls, during the cruise flight phase; however, offering an equivalent overall level of safety through compensation means (e.g., ground assistance, advanced cockpit design with workload alleviation means, pilot incapacitation detection). SiPOs are defined as end-to-end single-pilot operations. Air operations regulation already foresees conditions and limitations under which these types of operations are allowed. In the future, it is expected that these conditions and limitations will need to evolve to extend single-pilot operations to large airplanes, provided that compensation means (e.g., ground assistance, advanced cockpit design with workload alleviation means, capability to cope with pilot incapacitation) are in place in order to provide for an overall level of safety equivalent to today’s two-pilot operations. EASA is working with interested industry stakeholders to explore the feasibility of such operational concepts, while maintaining current safety levels. It is evident that both projects that rely heavily on AI and ML will need ATM support and therefore introduce new operational requirements. All these will impose a brand-new array of challenges to the ATM systems in the next years.


3. Method

We followed the CSE approach research methods [6,7,8] We used a range of meth-ods over several phases of fieldwork, documentation analysis and finally divergent thinking, comparative reasoning, and integrative thinking to compile a framework of cognitive functions of Air Traffic Controllers that can support adaptive Human-AI teaming.

We applied a documentation analysis of the most recent reports, white papers, po-sition papers and technical documents from ATM and aviation organizations [12, 13, 14, 15, 16, 17] about digitalization and AI/ML in the European continent. The next step was to perform a literature review. There is an extensive body of research CSE literature that can inform the development and application of automated systems which is beyond the scope of this paper. Hence, we decided to concentrate on some influential research publications and reports in the areas of automation AI/ML, CSE and ATM [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]. In the final phase, we performed an extensive literature review focused on models of human performance, team performance, decision-making, anomaly response and cognitive functions [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54 , 55, 56, 57, 58, 59]


4. A Framework for Supporting Adaptive Human-AI Teaming

The framework we propose examines six cognitive functions as follows:

  • Steering or Goal Setting.
  • Sensemaking and Mental Models.
  • Common Operating Picture or Shared Mental Models.
  • Coordination and Transfer of Control.
  • Managing Changes.
  • Operating or Planning-Doing-Checking cycle.

4.1 Steering or goal setting

An orientation of performance towards specific goals that can be modified later during events. It involves an assessment of the context of work in terms of threats, constraints, and resources. Sometimes explorative goals may be taken by air traffic controllers to see how the system reacts. Steering broadly refers to the choices and goals that people set in order to achieve their mission. In a complex environment, people may have to tackle multiple goals that compete for limited resources. To be successful in steering, people need to demonstrate willingness to make the necessary trade-offs and show an ability to spot and exploit leverage points [35]. Fundamental to steering is the presence of a large ‘set of action repertoires’ in the form of work practices honed through ‘enlightened’ experimentation [60].

AI could very well assist air traffic controllers in making more environment friendly decisions in situations where either solution method has no consequence on the overall traffic picture. If a conflict needs to be solved by either a level change or headings and it does not make a difference which one it is, AI would have the capability to advise which solution would be better for the environment based on the wind and atmospheric conditions. Making aviation more climate friendly is a goal made by the EU to which individual air traffic controllers have no chance to contribute to unless more assistance is provided in their decision making. Obviously, safety would come first, but with the way, we see AI potential in ATC as an assistant, air traffic controllers do not normally care for environmentally wise headings or a level change if there is no other traffic to consider.

4.2 Sensemaking and Mental Models

A process of interpretation of the situation to make sense of the problem and under-stand the factors that contributed to its occurrence. Sensemaking relies on the mental models of the air traffic controllers that are developed and refined over time. When information is incomplete or delayed, mental models are useful in filling gaps in understanding and testing hypotheses about causes and plausible effects. In dynamic situations, poor decisions are made when models remain outdated. Sensemaking relies on having an adequate ‘mental model’ of potential hazards, causes, available resources and risk control strategies. Mental models help controllers and flight crews challenge their understanding and remain vigilant to the possibility of failure. In ATC and aviation, a common problem is ‘failure to reframe’ a mental model or mindset, as new evidence becomes available. The initial situation assessment may seem appropriate, given the availability of information, but practitioners may fail to revise their mindsets. Many patterns of breakdown relate to problems in managing complexity and flawed mental models [61].

4.3 Common Operating Picture or shared mental models

A COP refers to a common perception of threats, available resources and forces, opportunities for action and assessments of work. The COP can create common orientation and help teams coordinate and converge their efforts to the overall mission. The COP is an important frame for making sense of a problem in a collective man-ner. The COP should incorporate information that enables situational information to be produced, visualized and presented in such a way that all information is available to all actors involved in the situation in real time. Flight decks and ATC operations rooms should support COP for practitioners. However, radar displays, decision sup-port tools, flight data displays and other inanimate objects are simply repositories from which practitioners gather information.

AI could assist air traffic controllers with establishing a clearer picture during weather avoidance. The weather radars available on controller working positions are extremely unreliable and currently not updated in real time. Displaying a full weather radar on the screen would cause too much clutter. If AI works in the background and synchronizes the most up to date weather radar and predicted traffic flows, it could assist the air traffic controllers by giving information on expected avoidance direction. This has the potential to allow the air traffic controller to manage the disrupted flows more efficiently (because the requests to avoid weather cells would not be unexpected) and reduce capacity restrictions. Within a single center, this assistant could warn air traffic controllers about certain headings that would most likely result in later weather avoidance in another sector and let the air traffic controller proactively offer a re-routing.

It would be great if AI could also collect PIREP information and NOTAMs on turbulence and provide air traffic controllers with information on which cruising levels to avoid. This would reduce air traffic controllers’ workload, as we would no longer climb aircraft to levels that they then need to descend out of shortly after. At the very least, air traffic controllers could use the information given by AI to provide more accurate information to pilots that ask about turbulence and then let them make their own decision about their cruising level.

4.4 Coordination and transfer of control

A team process that has been included to examine the coordination of multiple loops or units, the handover of work between shifts and the interaction of people and automation. Coordination and transfer of control is a complex process that involves team synchronization, handover of work between shifts and interaction with colleagues and automation. The very nature of teamwork and task allocation can generate many dependencies that require orchestrated action in order to converge toward the same goal.

4.5 Managing changes

A process for addressing changes in the system or the environment that may have an impact on performance. Adaptation usually takes the form of flexibility in changing behaviours between alternative modes of operation—for example, tight versus loose plans and feedforward versus feedback control modes. Adaptive teams monitor changes in the system or the environment and try to match their capabilities to them or even reserve their capabilities for anticipated events. The implication is that practitioners should retain some residual capacity for managing a number of secondary activities that have to do with correcting side effects and coping with task interruptions, including an assessment of one’s own capabilities and “margins for manoeuvre” [25].

For instance, sector load prediction could be made more reliable, as AI would have the capability to collect and cross-reference all the necessary data – predicted traffic count, complexity, weather, turbulence, etc. In essence, AI assistant could provide everything that a supervisor needs to know in order to decide to either open or close a sector. After collecting this data, the AI assistant can then provide advice on sector opening/closing, but the supervisor would make the final decision.

4.6 Planning-Doing-Checking cycle

A basic performance cycle that involves devising a plan to implement a certain goal, executing the plan and monitoring its effects. Implementation refers to manual and tracking activities necessary to achieve the action targets set in the plans. Tracking activities should respect the constraints of space and time in the work domain. Implementation relies on feedback control where target-outcome gaps are corrected in time. The basic Planning- Doing-Checking (PDC) cycle underlies all types of performance where people devise a plan of action to use their resources within certain constrains, execute the plan in a timely fashion and check over or evaluate their work progress.


5. Discussion and Conclusion

Changes in the ATM domain are of permanent nature and challenges of research, development, and transition to introduce these changes are a daily life for Air navi-gation Service Providers and their staff. Be it Air Traffic Controllers, Technicians, Engineers, managers, and Decision makers. Automation is nothing new in the ATM system. The so- called New Technologies leading digitalization, including AI and ML are finding their ways into the ATM working environment. Whereas lot of expectation is linked to a so-called technology hype introduction of new technology will have to follow the path of introducing new technological component into a running ATM system. Linked to the regulatory and certification challenges, a lot of the modern technology will have to be interwoven into the existing architecture. This will create new challenges, surprises and will not escape the rough journey of in-creased automated system in ATM.

One of the driving arguments for the introduction new technology is that costs of production are reduced because there are fewer Air Traffic Controllers’ costs – be it training, the reliability and inefficiency of the practitioner. Designs that seek to op-timize managerial values can have the effect -intentional or otherwise -to privilege the managerial objectives and in doing so constrain the humanistic design. The consequences of this are that the practitioner’ degrees of freedom are reduced; buffers and margins are impacted in ways that limit the ability of the system to maintain and sustain adaptability when confronted with uncertainty and surprise events and there-by making the system less effective [62]. Additionally, increasing the distance be-tween the Air Traffic Controllers, and the system reduces the practitioners’ ability to intervene in case of unexpected events.

When work changes, as in the case of introduction of modern technology in the operations room, there are consequences on the practitioner’s ability to create strategies that can exploit system characteristics of agility and flexibility, in other words adaptive capacity. Boy [63] refers to this as a form of smart integration: designing for innovative complex systems – that exploit the ability to understand increasing complexity. This means embracing complexity. A design that embraces complexity will adopt the opposite of the reductionist view – which means reducing or eliminating the effects of complexity, by eliminating or reducing the role of the human. As opposed to designs that embrace and design for complexity by matching emerging system behaviours with creative emergent human real time responses.

The findings of this study are pending further validation and generalization due to the exploratory character of research. Any associations and inferences drawn from this study are expected to remain relatively stable when studies of introducing new technology to OPS rooms are carried out in live settings. It is also hoped that this framework for supporting adaptive Human-AI Teaming could provide a viable solu-tion to the efficient introduction of innovative technology in the OPS rooms.


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Last Update: September 26, 2023  

September 17, 2023   98   Jean-Francois Lepage    2023    

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