Owning Decisions

AI Decision-Support and the Attributability-Gap

authored by

Jannik Zeiser

Abstract

Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine’s behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today’s AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call “decision ownership”: they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made.

Organisation(s)

Institute of Philosophy

Type

Article

Journal

Science and engineering ethics

Volume

30

No. of pages

19

ISSN

1353-3452

Publication date

08.2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Health(social science), Issues, ethics and legal aspects, Health Policy, Management of Technology and Innovation

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1007/s11948-024-00485-1 (Access: Open)