Featured research (5)
Supporting pilots with a decision support tool (DST) during high-workload scenarios is a promising and potentially very helpful application for AI in aviation. Nevertheless, design requirements and opportunities for trustworthy DSTs within the aviation domain have not been explored much in the scientific literature. To address this gap, we explore the decision-making process of pilots with respect to user requirements for the use case of diversions. We do so via two prototypes, each representing a role the AI could have in a DST: A) Unobtrusively hinting at data points the pilot should be aware of. B) Actively suggesting and ranking diversion options based on criteria the pilot has previously defined. Our work-in-progress feedback study reveals four preliminary main findings: 1) Pilots demand guaranteed trustworthiness of such a system and refuse trust calibration in the moment of emergency. 2) We may need to look beyond trust calibration for isolated decision points and rather design for the process leading to the decision. 3) An unobtrusive, augmenting AI seems to be preferred over an AI proposing and ranking diversion options at decision time. 4) Shifting the design goal toward supporting situation awareness rather than the decision itself may be a promising approach to increase trust and reliance.
As the aviation industry is actively working on adopting AI for air traffic, stakeholders agree on the need for a human-centered approach. However, automation design is often driven by user-centered intentions, while the development is actually technology-centered. This can be attributed to a discrepancy between the system designers’ perspective and complexities in real-world use. The same can be currently observed with AI applications where most design efforts focus on the interface between humans and AI, while the overall system design is built on preconceived assumptions. To understand potential usability issues of AI-driven cockpit assistant systems from the users’ perspective, we conducted interviews with four experienced pilots. While our participants did discuss interface issues, they were much more concerned about how autonomous systems could be a burden if the operational complexity exceeds their capabilities. Besides commonly addressed human-AI interface issues, our results thus point to the need for more consideration of operational complexities on a system-design level.
Decision support systems based on AI are usually designed to generate complete outputs entirely automatically and to explain those to users. However, explanations, no matter how well designed, might not adequately address the output uncertainty of such systems in many applications. This is especially the case when the human-out-of-the-loop problem persists, which is a fundamental human limitation. There is no reason to limit decision support systems to such backward reasoning designs, though. We argue how more interactive forward reasoning designs where users are actively involved in the task can be effective in managing output uncertainty. We therefore call for a more complete view of the design space for decision support systems that includes both backward and forward reasoning designs. We argue that such a more complete view is necessary to overcome the barriers that hinder AI deployment especially in high-stakes applications.
Given the opaqueness and complexity of modern AI algorithms, there is currently a strong focus on developing transparent and explainable AI, especially in high-stakes domains. We claim that opaqueness and complexity are not the core issues for end users when interacting with AI. Instead, we propose that the output uncertainty inherent to AI systems is the actual problem, with opaqueness and complexity as contributing factors. Transparency and explainability should therefore not be the end goals, as such a focus tends to place the human into a passive supervisory role in what is in reality an algorithm-centered system design. To enable effective management of output uncertainty, we believe it is necessary to focus on truly human-centered AI designs that keep the human in an active role of control. We discuss the conceptual implications of such a shift in focus and give examples from literature to illustrate the more holistic, interactive designs that we envision.