Cargando…
Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability
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 respon...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417397/ https://www.ncbi.nlm.nih.gov/pubmed/31650511 http://dx.doi.org/10.1007/s11948-019-00146-8 |
_version_ | 1783569483585028096 |
---|---|
author | Coeckelbergh, Mark |
author_facet | Coeckelbergh, Mark |
author_sort | Coeckelbergh, Mark |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7417397 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-74173972020-08-17 Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability Coeckelbergh, Mark Sci Eng Ethics Original Research/Scholarship 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. Springer Netherlands 2019-10-24 2020 /pmc/articles/PMC7417397/ /pubmed/31650511 http://dx.doi.org/10.1007/s11948-019-00146-8 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Research/Scholarship Coeckelbergh, Mark Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability |
title | Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability |
title_full | Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability |
title_fullStr | Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability |
title_full_unstemmed | Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability |
title_short | Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability |
title_sort | artificial intelligence, responsibility attribution, and a relational justification of explainability |
topic | Original Research/Scholarship |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417397/ https://www.ncbi.nlm.nih.gov/pubmed/31650511 http://dx.doi.org/10.1007/s11948-019-00146-8 |
work_keys_str_mv | AT coeckelberghmark artificialintelligenceresponsibilityattributionandarelationaljustificationofexplainability |