To Give or Not to Give? The Impacts of Strategically Withheld Recourse
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915233459601408 |
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| author | Chen, Yatong Estornell, Andrew Vorobeychik, Yevgeniy Liu, Yang |
| author_facet | Chen, Yatong Estornell, Andrew Vorobeychik, Yevgeniy Liu, Yang |
| contents | Individuals often aim to reverse undesired outcomes in interactions with automated systems, like loan denials, by either implementing system-recommended actions (recourse), or manipulating their features. While providing recourse benefits users and enhances system utility, it also provides information about the decision process that can be used for more effective strategic manipulation, especially when the individuals collectively share such information with each other.
We show that this tension leads rational utility-maximizing systems to frequently withhold recourse, resulting in decreased population utility, particularly impacting sensitive groups.
To mitigate these effects, we explore the role of recourse subsidies, finding them effective in increasing the provision of recourse actions by rational systems, as well as lowering the potential social cost and mitigating unfairness caused by recourse withholding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05891 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | To Give or Not to Give? The Impacts of Strategically Withheld Recourse Chen, Yatong Estornell, Andrew Vorobeychik, Yevgeniy Liu, Yang Computer Science and Game Theory Machine Learning Individuals often aim to reverse undesired outcomes in interactions with automated systems, like loan denials, by either implementing system-recommended actions (recourse), or manipulating their features. While providing recourse benefits users and enhances system utility, it also provides information about the decision process that can be used for more effective strategic manipulation, especially when the individuals collectively share such information with each other. We show that this tension leads rational utility-maximizing systems to frequently withhold recourse, resulting in decreased population utility, particularly impacting sensitive groups. To mitigate these effects, we explore the role of recourse subsidies, finding them effective in increasing the provision of recourse actions by rational systems, as well as lowering the potential social cost and mitigating unfairness caused by recourse withholding. |
| title | To Give or Not to Give? The Impacts of Strategically Withheld Recourse |
| topic | Computer Science and Game Theory Machine Learning |
| url | https://arxiv.org/abs/2504.05891 |