Owning Decisions: AI Decision-Support and the Attributability-Gap.
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 ca...
| Publicado en: | Science & Engineering Ethics Vol. 30; no. 4; pp. 1 - 20 |
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| Formato: | Artículo |
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Springer Nature
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=179385994&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 179385994 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 13533452 GNI jtl: Science & Engineering Ethics issn: 13533452 maglogo: N pubinfo: dt: Aug2024 vid: 30 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 179385994 10.1007/s11948-024-00485-1 ppf: 1 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P size: 900KB tig: atl: Owning Decisions: AI Decision-Support and the Attributability-Gap. aug: au: Zeiser, Jannik affil: https://ror.org/0304hq317 Leibniz Universität Hannover, Institut für Philosophie, Im Moore 21, 30167, Hannover, Germany sug: keyword: Accountability Agency Artificial intelligence Attributability Decision-support systems Machine learning Responsibility ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Science & Engineering Ethics is a copyright of Springer, 2024. All Rights Reserved. item: Science & Engineering Ethics holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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