In this paper, we first classify different types of second opinions and evaluate the ethical and epistemological implications of providing those in a clinical context. Second, we discuss the issue of how artificial intelligent (AI) could replace the human cognitive labour of providing such second opinion and find that several AI reach the levels of accuracy and efficiency needed to clarify their use an urgent ethical issue. Third, we outline the normative conditions of how AI may be used as second opinion in clinical processes, weighing the benefits of its efficiency against concerns of responsibility attribution. Fourth, we provide a ‘rule of disagreement’ that fulfils these conditions while retaining some of the benefits of expanding the use of AI-based decision support systems (AI-DSS) in clinical contexts. This is because the rule of disagreement proposes to use AI as much as possible, but retain the ability to use human second opinions to resolve disagreements between AI and physician-in-charge. Fifth, we discuss some counterarguments.

The increased presence of medical AI in clinical use raises the ethical question which standard of explainability is required for an acceptable and responsible implementation of AI-based applications in medical contexts. In this paper, we elaborate on the emerging debate surrounding the standards of explainability for medical AI. For this, we first distinguish several goods explainability is usually considered to contribute to the use of AI in general, and medical AI in specific. Second, we propose to understand the value of explainability relative to other available norms of explainable decision-making. Third, in pointing out that we usually accept heuristics and uses of bounded rationality for medical decision-making by physicians, we argue that the explainability of medical decisions should not be measured against an idealized diagnostic process, but according to practical considerations. We conclude, fourth, to resolve the issue of explainability-standards by relocating the issue to the AI’s certifiability and interpretability.

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The ethical debate about technologies called artificial intelligence (AI) has recently turned towards the question whether and in which sense using AI can be sustainable, distinguishing possible contributions of AI to achieve the end of sustainability on the one hand from the sustainability of AI and its underlying technologies as means on the other hand. This important distinction is both applied in the context of environmental as well as social sustainability. However, further elaboration is necessary to capture the complexities of sustainability assessments in the context of AI. To this end, our analysis of the ends and means of “sustainable AI” in social and environmental contexts leads to a matrix of four dimensions reflecting its social and its environmental impact and costs. This matrix avoids overly narrow, one-dimensional assessments that too quickly label some AI-based technology as sustainable. While a selective assessment can, at best, warrant the narrower verdict of “thin” sustainability, only such a comprehensive assessment can warrant the verdict of what we call “thick” sustainability. In consequence, we recommend to broaden the normative scope in considering the ethics and justice of AI and to use the notion “sustainability” more carefully and sparingly, and to pursue the more ambitious goal of “thick” sustainability of AI-based technologies to meaningfully contribute to actual improvements of human lives and living together. Current conditions of an economy oriented towards permanent growth, however, may make it difficult or even impossible to realise sustainable AI.

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