Contestable AI needs Computational Argumentation
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914537980035072 |
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| author | Leofante, Francesco Ayoobi, Hamed Dejl, Adam Freedman, Gabriel Gorur, Deniz Jiang, Junqi Paulino-Passos, Guilherme Rago, Antonio Rapberger, Anna Russo, Fabrizio Yin, Xiang Zhang, Dekai Toni, Francesca |
| author_facet | Leofante, Francesco Ayoobi, Hamed Dejl, Adam Freedman, Gabriel Gorur, Deniz Jiang, Junqi Paulino-Passos, Guilherme Rago, Antonio Rapberger, Anna Russo, Fabrizio Yin, Xiang Zhang, Dekai Toni, Francesca |
| contents | AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI guidelines (e.g. by the OECD) and regulation of automated decision-making (e.g. GDPR). In this position paper we explore how contestability can be achieved computationally in and for AI. We argue that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes, whereby machines can (i) interact with humans and/or other machines to progressively explain their outputs and/or their reasoning as well as assess grounds for contestation provided by these humans and/or other machines, and (ii) revise their decision-making processes to redress any issues successfully raised during contestation. Given that much of the current AI landscape is tailored to static AIs, the need to accommodate contestability will require a radical rethinking, that, we argue, computational argumentation is ideally suited to support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10729 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contestable AI needs Computational Argumentation Leofante, Francesco Ayoobi, Hamed Dejl, Adam Freedman, Gabriel Gorur, Deniz Jiang, Junqi Paulino-Passos, Guilherme Rago, Antonio Rapberger, Anna Russo, Fabrizio Yin, Xiang Zhang, Dekai Toni, Francesca Artificial Intelligence AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI guidelines (e.g. by the OECD) and regulation of automated decision-making (e.g. GDPR). In this position paper we explore how contestability can be achieved computationally in and for AI. We argue that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes, whereby machines can (i) interact with humans and/or other machines to progressively explain their outputs and/or their reasoning as well as assess grounds for contestation provided by these humans and/or other machines, and (ii) revise their decision-making processes to redress any issues successfully raised during contestation. Given that much of the current AI landscape is tailored to static AIs, the need to accommodate contestability will require a radical rethinking, that, we argue, computational argumentation is ideally suited to support. |
| title | Contestable AI needs Computational Argumentation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2405.10729 |