Human machine teaming in the air traffic control operations rooms: The IFATCA’s perspective
Bibliographic record
Abstract
An adaptive 21 st century organization is archetypically digitally powered, leading many organizations to pursue digital transformation. Air Navigation Service Providers (ANSPs), which are the building blocks of the Air Traffic Management (ATM) system, are not an exception to this rule. Sustained adaptability is a relentless call for ANSPs and refers to the ability to continue to adapt to changing environments, stakeholders, demands, contexts, and constraints within the wider aviation system. In this context Artificial Intelligence (AI) and Machine learning (ML) are finding their way into the ATM operational environment. This paper presents the results of an initial attempt to design a Human Machine Teaming (HMT) guide in the ATM domain. The aim of the HMT guide is to assist Air Traffic Controllers in integrating technology in the various forms of new intelligent, autonomous systems, automation and AI/ML that can work in adaptive partnership with the human practitioners in the operations rooms. We followed a Cognitive Systems Engineering (CSE) approach to develop an HMT guide based on a set of generic principles and an iterative process. We used a range of methods over several phases of fieldwork, documentation analysis and finally divergent thinking, comparative reasoning, and integrative thinking to compile a set of generic principles and an iterative process of four stages before fielding a technology system in the operations rooms. The proposed HMT framework could provide a viable solution to the efficient introduction of innovative technology in the Air Traffic Control Operations Rooms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".