Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which (...) levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements. (shrink)

To address the tension between demands for more transparent AI systems and the aim to develop and design robots with apparent agency for smooth and intuitive human-robot interaction (HRI), we present in this paper an argument for why explainability in HRI would benefit from being question-centered. First, we review how explainability has been discussed in AI and HRI respectively, to then present the challenge in HRI to accommodate the requirement of transparency while also keeping up the appearance of the robot (...) as a social agent. Based on the conceptualization of questions as information-seeking acts, our proposal for a question-centered approach to explainable HRI (xHRI) implies that to ensure transparency, robots shall provide explanations upon request in the form of questioning. We end this paper by laying out a preliminary taxonomy of xHRI-relevant questions that present the kinds of explanations a robot can or should be able to provide for successful and transparent HRI. (shrink)