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.

This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.

The purpose of image restoration is to recover the original state of damaged images. To overcome the disadvantages of the traditional, manual image restoration process, like the high time consumption and required domain knowledge, automatic inpainting methods have been developed. These methods, however, can have limitations for complex images and may require a lot of input data. To mitigate those, we present "interactive Deep Image Prior", a combination of manual and automated, Deep-Image-Prior-based restoration in the form of an interactive process with the human in the loop. In this process a human can iteratively embed knowledge to provide guidance and control for the automated inpainting process. For this purpose, we extended Deep Image Prior with a user interface which we subsequently analyzed in a user study. Our key question is whether the interactivity increases the restoration quality subjectively and objectively. Secondarily, we were also interested in how such a collaborative system is perceived by users.