Responsibility in a Multi-Value Strategic Setting
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910691758178304 |
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| author | Parker, Timothy Grandi, Umberto Lorini, Emiliano |
| author_facet | Parker, Timothy Grandi, Umberto Lorini, Emiliano |
| contents | Responsibility is a key notion in multi-agent systems and in creating safe, reliable and ethical AI. However, most previous work on responsibility has only considered responsibility for single outcomes. In this paper we present a model for responsibility attribution in a multi-agent, multi-value setting. We also expand our model to cover responsibility anticipation, demonstrating how considerations of responsibility can help an agent to select strategies that are in line with its values. In particular we show that non-dominated regret-minimising strategies reliably minimise an agent's expected degree of responsibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17229 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Responsibility in a Multi-Value Strategic Setting Parker, Timothy Grandi, Umberto Lorini, Emiliano Artificial Intelligence Responsibility is a key notion in multi-agent systems and in creating safe, reliable and ethical AI. However, most previous work on responsibility has only considered responsibility for single outcomes. In this paper we present a model for responsibility attribution in a multi-agent, multi-value setting. We also expand our model to cover responsibility anticipation, demonstrating how considerations of responsibility can help an agent to select strategies that are in line with its values. In particular we show that non-dominated regret-minimising strategies reliably minimise an agent's expected degree of responsibility. |
| title | Responsibility in a Multi-Value Strategic Setting |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2410.17229 |