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

• Published in: Science and engineering ethics Vol. 26; no. 4; pp. 2051 - 2068
• Main Author: Coeckelbergh, Mark
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2020; Springer Nature B.V
• Subjects: Agents (artificial intelligence); Artificial intelligence; Automation; Autonomous vehicles; Biomedical Engineering and Bioengineering; Decision making; Education; Engineering; Ethics; Machine learning; Medicine/Public Health; Original Research/Scholarship; Patients; Philosophy; Philosophy of Science; Software; Moral patiency; Problem of many hands; Transparency; Artificial intelligence (AI); Responsibility conditions; Moral agency; Responsibility; Answerability; Explainability; Responsibility attribution
• ISSN: 1353-3452; 1471-5546; 1471-5546
• DOI: 10.1007/s11948-019-00146-8

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.