Online Learning with Multiple Fairness Regularizers via Graph-Structured Feedback
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916037451055104 |
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| author | Zhou, Quan Marecek, Jakub Shorten, Robert |
| author_facet | Zhou, Quan Marecek, Jakub Shorten, Robert |
| contents | There is an increasing need to enforce multiple, often competing, measures of fairness within automated decision systems. The appropriate weighting of these fairness objectives is typically unknown a priori, may change over time and, in our setting, must be learned adaptively through sequential interactions. In this work, we address this challenge in a bandit setting, where decisions are made with graph-structured feedback. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Learning with Multiple Fairness Regularizers via Graph-Structured Feedback Zhou, Quan Marecek, Jakub Shorten, Robert Machine Learning Artificial Intelligence There is an increasing need to enforce multiple, often competing, measures of fairness within automated decision systems. The appropriate weighting of these fairness objectives is typically unknown a priori, may change over time and, in our setting, must be learned adaptively through sequential interactions. In this work, we address this challenge in a bandit setting, where decisions are made with graph-structured feedback. |
| title | Online Learning with Multiple Fairness Regularizers via Graph-Structured Feedback |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.14311 |