When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data
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arXiv
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| Format: | Preprint |
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2022
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| author | Awad, Edmond Levine, Sydney Loreggia, Andrea Mattei, Nicholas Rahwan, Iyad Rossi, Francesca Talamadupula, Kartik Tenenbaum, Joshua Kleiman-Weiner, Max |
| author_facet | Awad, Edmond Levine, Sydney Loreggia, Andrea Mattei, Nicholas Rahwan, Iyad Rossi, Francesca Talamadupula, Kartik Tenenbaum, Joshua Kleiman-Weiner, Max |
| contents | One of the most remarkable things about the human moral mind is its flexibility. We can make moral judgments about cases we have never seen before. We can decide that pre-established rules should be broken. We can invent novel rules on the fly. Capturing this flexibility is one of the central challenges in developing AI systems that can interpret and produce human-like moral judgment. This paper details the results of a study of real-world decision makers who judge whether it is acceptable to break a well-established norm: ``no cutting in line.'' We gather data on how human participants judge the acceptability of line-cutting in a range of scenarios. Then, in order to effectively embed these reasoning capabilities into a machine, we propose a method for modeling them using a preference-based structure, which captures a novel modification to standard ``dual process'' theories of moral judgment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_07763 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data Awad, Edmond Levine, Sydney Loreggia, Andrea Mattei, Nicholas Rahwan, Iyad Rossi, Francesca Talamadupula, Kartik Tenenbaum, Joshua Kleiman-Weiner, Max Artificial Intelligence Computers and Society One of the most remarkable things about the human moral mind is its flexibility. We can make moral judgments about cases we have never seen before. We can decide that pre-established rules should be broken. We can invent novel rules on the fly. Capturing this flexibility is one of the central challenges in developing AI systems that can interpret and produce human-like moral judgment. This paper details the results of a study of real-world decision makers who judge whether it is acceptable to break a well-established norm: ``no cutting in line.'' We gather data on how human participants judge the acceptability of line-cutting in a range of scenarios. Then, in order to effectively embed these reasoning capabilities into a machine, we propose a method for modeling them using a preference-based structure, which captures a novel modification to standard ``dual process'' theories of moral judgment. |
| title | When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2201.07763 |