Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability

By Mark Coeckelbergh. Science and Engineering Ethics, 2019

366 citations recorded by the source corpus.

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence (AI) technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which draws attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or “patients” of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency.

Subfields: Cognitive Neuroscience, Health Informatics, Safety Research

View Paper: https://www.semanticscholar.org/paper/672d7db068a6ee6eab17c462ef6ecd118bd005bb

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Citations in the corpus

Towards Transparency by Design for Artificial Intelligence (2020, background): “Automated decision making-systems can have impacts on individuals and society at large, creating novel ethical challenges that open up fundamental questions regarding responsibility, human dignity, and the relation between humans and machines (Coeckelbergh 2020; Matthias 2004; Laton-ero 2018).”
Ethics and governance of trustworthy medical artificial intelligence (2023, background): “Therefore, AI cannot be a responsible subject [65].”
Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them (2021, background): “…& Scharre, 2015; Moyes, 2016; Schwarz, 2018), MHC has been recently investigated in the field of automated driving systems (Calvert et al., 2018, 2019, 2020; Heikoop et al., 2019; Mecacci & Santoni de Sio, 2020; Santoni de Sio & van den Hoven, 2018) and medical automation (Braun et al., 2020;…”