When a computer system causes harm, who is responsible? This question has renewed significance given the proliferation of autonomous systems enabled by modern artificial intelligence techniques. At the root of this problem is a philosophical difficulty known in the literature as the responsibility gap. That is to say, because of the causal distance between the designers of autonomous systems and the eventual outcomes of those systems, the dilution of agency within the large and complex teams that design autonomous systems, and (...) the impossibility of fully predicting how autonomous systems will behave once deployed, determining who is morally responsible for harms caused by autonomous systems is unclear at a conceptual level. I review past work on this topic, criticizing prior works for suggesting workarounds rather than philosophical answers to the conceptual problem presented by the responsibility gap. The view I develop, drawing on my earlier work on vicarious moral responsibility, explains why computing professionals are ethically required to take responsibility for the systems they design, despite not being blameworthy for the harms these systems may cause. (shrink)

Bioethics has developed approaches to address ethical issues in health care, similar to how technology ethics provides guidelines for ethical research on artificial intelligence, big data, and robotic applications. As these digital technologies are increasingly used in medicine, health care and public health, thus, it is plausible that the approaches of technology ethics have influenced bioethical research. Similar to the “empirical turn” in bioethics, which led to intense debates about appropriate moral theories, ethical frameworks and meta-ethics due to the increased (...) use of empirical methodologies from social sciences, the proliferation of health-related subtypes of technology ethics might have a comparable impact on current bioethical research. This systematic journal review analyses the reporting of ethical frameworks and non-empirical methods in argument-based research articles on digital technologies in medicine, health care and public health that have been published in high-impact bioethics journals. We focus on articles reporting non-empirical research in original contributions. Our aim is to describe currently used methods for the ethical analysis of ethical issues regarding the application of digital technologies in medicine, health care and public health. We confine our analysis to non-empirical methods because empirical methods have been well-researched elsewhere. Finally, we discuss our findings against the background of established methods for health technology assessment, the lack of a typology for non-empirical methods as well as conceptual and methodical change in bioethics. Our descriptive results may serve as a starting point for reflecting on whether current ethical frameworks and non-empirical methods are appropriate to research ethical issues deriving from the application of digital technologies in medicine, health care and public health. (shrink)

This paper builds upon recent work in narrative theory and the philosophy of technology by examining the place of transparency and responsibility in discussions of AI, and what some of the implications of this might be for thinking ethically about AI and especially AI practices, that is, the structured social activities implicating and defining what AI is. In this paper, we aim to show how pursuing a narrative understanding of technology and AI can support knowledge of process and practice through (...) transparency, as well help summon us to responsibility through visions of possibility and of actual harms arising from AI practices. We provide reflections on the relations between narrative, transparency and responsibility, building an argument that narratives (about AI, practices, and those persons implicated in its design, implementation, and deployment) support the kind of knowing and understanding that is the aim of transparency, and, moreover, that such knowledge supports responsibility in informing agents and activating responsibility through creating knowledge about something that can and should be responded to. Furthermore, we argue for considering an expansion of the kinds of practices that we might legitimately consider ‘AI practices’ given the diverse set of (often materially embedded) activities that sustain and are sustained by AI that link directly to its ethical acceptability and which are rendered transparent in the narrative mode. Finally, we argue for an expansion of narratives and narrative sources to be considered in questions of AI, understanding that transparency is multi-faceted and found in stories from diverse sources and people. (shrink)