Transparency of a robot's actions and intentions is important for trustful human-robot collaboration. Current approaches for creating transparency through explanations mostly follow the approach "more information creates more transparency", assuming cognitive load remains manageable. However, this ignores human active reasoning, which can, if not too demanding, create a better understanding from less information, as found in education literature. To explore this self-explanation effect, we compared three explanation structures (fully-specified, under-specified, no explanation) that induced different levels of active reasoning demand (low, medium, high). In a controlled laboratory study, 36 participants observed a robot completing rule-based classification tasks of varying difficulty (easy, moderate, hard) across all explanation conditions. We found that a moderate reasoning demand, elicited through under-specified explanations, produced the best understanding of the robot's actions compared to full or no explanations. This challenges the current "more helps more" approach and may help design more effective explanations for creating transparency.

ExtendAI makes plan for action extends user's plan by embedding feedback makes sense of plan containing AI's feedback makes final decision RecommendAI makes sense of AI's suggestions makes suggestion for action makes final decision Figure 1: Illustrative comparison of the thought process when interacting with two 'types' of AI -RecommendAI and ExtendAI.

AI is anticipated to enhance human decision-making in high-stakes domains like aviation, but adoption is often hindered by challenges such as inappropriate reliance and poor alignment with users' decision-making. Recent research suggests that a core underlying issue is the recommendation-centric design of many AI systems, i.e., they give end-to-end recommendations and ignore the rest of the decision-making process. Alternative support paradigms are rare, and it remains unclear how the few that do exist compare to recommendation-centric support. In this work, we aimed to empirically compare recommendation-centric support to an alternative paradigm, continuous support, in the context of diversions in aviation. We conducted a mixed-methods study with 32 professional pilots in a realistic setting. To ensure the quality of our study scenarios, we conducted a focus group with four additional pilots prior to the study. We found that continuous support can support pilots' decision-making in a forward direction, allowing them to think more beyond the limits of the system and make faster decisions when combined with recommendations, though the forward support can be disrupted. Participants' statements further suggest a shift in design goal away from providing recommendations, to supporting quick information gathering. Our results show ways to design more helpful and effective AI decision support that goes beyond end-to-end recommendations.

Intelligent decision support tools (DSTs) hold the promise to improve the quality of human decision-making in challenging situations like diversions in aviation. To achieve these improvements, a common goal in DST design is to calibrate decision makers’ trust in the system. However, this perspective is mostly informed by controlled studies and might not fully reflect the real-world complexity of diversions. In order to understand how DSTs can be beneficial in the view of those who have the best understanding of the complexity of diversions, we interviewed professional pilots. To facilitate discussions, we built two low-fidelity prototypes, each representing a different role a DST could assume: (a) actively suggesting and ranking airports based on pilot-specified criteria, and (b) unobtrusively hinting at data points the pilot should be aware of. We find that while pilots would not blindly trust a DST, they at the same time reject deliberate trust calibration in the moment of the decision. We revisit appropriation as a lens to understand this seeming contradiction as well as a range of means to enable appropriation. Aside from the commonly considered need for transparency, these include directability and continuous support throughout the entire decision process. Based on our design exploration, we encourage to expand the view on DST design beyond trust calibration at the point of the actual decision.