Der Einsatz Synthetischer Aufgabenumgebungen zur Untersuchung kollaborativer Prozesse in Leitzentralen am Beispiel der "generic Control Center Task Environment" (ConCenT)

Leitzentralen sind sozio-technische Systeme, deren Effektivität in großem Maße von den Fähigkeiten ihrer Mitarbeiter zum koordinierten Handeln abhängt. Es wurde eine synthetische Aufgabenumgebung ConCenT entwickelt, welche darauf ausgelegt ist, Koordinationsmuster im kognitiven System „Team“ im Kontext einer sich entfaltenden Aufgabenbearbeitung anhand von übergeordneten Blickbewegungsmustern erkennbar und messbar zu machen. Der Schwerpunkte des Beitrags liegt in der Vorstellung des Entwicklungsprozesses und der Versuchsumgebung ConCenT (generic Control Center Task Environment) und endet mit einem Ausblick auf geplante experimentelle Studien

Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work

The increasing prevalence of Artificial Intelligence (AI) in safety-critical contexts such as air-traffic control leads to systems that are practical and efficient, and to some extent explainable to humans to be trusted and accepted. The present structured literature analysis examines n = 236 articles on the requirements for the explainability and acceptance of AI. Results include a comprehensive review of n = 48 articles on information people need to perceive an AI as explainable, the information needed to accept an AI, and representation and interaction methods promoting trust in an AI. Results indicate that the two main groups of users are developers who require information about the internal operations of the model and end users who require information about AI results or behavior. Users' information needs vary in specificity, complexity, and urgency and must consider context, domain knowledge, and the user's cognitive resources. The acceptance of AI systems depends on information about the system's functions and performance, privacy and ethical considerations, as well as goal-supporting information tailored to individual preferences and information to establish trust in the system. Information about the system's limitations and potential failures can increase acceptance and trust. Trusted interaction methods are human-like, including natural language, speech, text, and visual representations such as graphs, charts, and animations. Our results have significant implications for future human-centric AI systems being developed. Thus, they are suitable as input for further application-specific investigations of user needs

Investigating Transactive Memory Systems of Multiteam Systems in Aviation

The highly effective air traffic depends and will also depend in the future on the collaboration of human protagonists from different organizations. The project “Inter-Team Collaboration” (ITC) aims to provide system engineers with tools and concepts for human factors that allow systemic access to the social side of socio-technical systems. A main question is how to induce collaborative decision-making within the dynamic environment of air traffic management (ATM) in order to make it more adaptive and resilient. Research design A crucial factor for collaboration in multi-team systems is the transactive memory system (TMS), built up and maintained by the interactions between team members as well as multi-team members. There are currently plans to develop an intervention which would facilitate the formation of TMS structures within the multi-team. Furthermore, a set of methods will be used to assess the TMS structures and communication processes in the laboratory studies and field cases. A laboratory study is planned to examine the initial TMS intervention under controlled conditions with non-expert participants. In addition to the laboratory study, we will use large-scale simulations in the context of three use-cases with experienced operators in order to investigate these methods. The three cases are, first, the Airport Control Center for Airport Management, second, the sector-less, time-based control of aircraft, and third, the Multiple-Remote-Tower Center. Results / Practical Implications / Relevance By using real-time simulations with operational experts, the results will have implication what kind of intervention is appropriate for enhancing TMS structures of MTS in aviation and how can TMS structures be measured? In conclusion, the ITC project presents the opportunity to investigate these topics in an interdisciplinary team as well as to bring together different capabilities and competencies for the purpose of investigating transactive memory systems of MTS in aviation on different levels and in an iterative process

User Participation in the Design of Trustworthy Human-AI-Collaboration in Air-Traffic Control

The introduction of AI-based systems is one of the core elements in the course of digital transformation in aviation. Human factors expertise is needed to find user-centered approaches of human-AI collaboration. The DLR project Collaboration of human operators and AI systems (LOKI) aims at developing concepts, demonstrators and prototypes for trustworthy human-AI collaboration in air traffic control. To consider users’ perspective in early design phases, two workshops with experienced air traffic controllers of Deutsche Flugsicherung GmbH (DFS) and Austro Control were conducted. The metaplan method was used to asses users’ expectations and their requirements on Human-AI collaboration. In both workshops, ten air traffic controllers participated. The workshops highlighted what users expect from trustworthy Human-AI collaboration in Air Traffic Control. The main results were users’ assessment which of their tasks could be delegated to an AI system and how the delegation of tasks to AI-systems should be designed. The workshops were a success among the participants as they provided insight into future requirements, responsibilities and tasks. Furthermore, they helped outline potential challenges in the interaction concept that must be solved to integrate AI systems successfully in aviation. These findings will be used for the concept design of prototypes

Global Patterns and Controls of Nutrient Immobilization On Decomposing Cellulose In Riverine Ecosystems

Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature

How to measure monitoring performance of pilots and air traffic controllers

In prior research on the future of aviation it was established that operators will have to work with highly automated systems. Increasing automation will require operators monitoring appropriately (OMA). OMA are expected to demonstrate the use of distinctly different monitoring phases (orientation, anticipation, detection, and recheck). Within these phases, they must grasp in time the relevant information that would enable them to take control should automation fail. The presented study aims at finding appropriate measurements for the identification of OMA on the basis of eye tracking. In order to do this, a normative model of adequate monitoring behavior was designed including the definition of areas of interest. We tested 90 participants who had to monitor a dynamic automatic process, and then take control. In order to decide on suitable eye tracking parameters it was asked which parameters are significantly related to manual control performance. The results show that the suitability of parameters depends on the specific phase of the monitoring process. Gaze durations allow for differentiating between high and low performing subjects during orientation phases. In contrast, relative fixation counts are suitable for predicting monitoring performance during detection phases. In general, the results support the assumption that eye tracking parameters are appropriate for identifying OMA

Eye movement indicators for successful failure detection

It is becoming increasingly important for pilots and air traffic controllers (ATCs) to be able to detect automation failures in a timely manner. In the context of personnel selection, conventional tests based on behavioural indicators could be complemented by integrating eye-tracking methods. The present study focuses on revealing eye movement parameters that reflect adequate scanning behaviour, which, in turn, predicts accurate failure detection. Eye movements were recorded whilst subjects were monitoring an automated system and reporting failures. Based on predefined areas of interest (AOIs), eye movement parameters were analyzed within different time units around the automation failure. The data suggest that there are differences between the eye movements of operators who detected automation failures and those who missed them. Human operators who successfully detect an automation failure demonstrate time-specific monitoring patterns. These patterns are quantified by parameters such as fixations counts, gaze durations, mean fixation durations, and the total time to first fixation. Depending on the time frame, different eye tracking parameters become relevant for failure detection, thus reflecting the interplay of the diverse cognitive processes involved. The findings are discussed in the context of the personnel selection and training of aviation operatives, as well as ATC incident reporting