Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910276454973440 |
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| author | Lv, Xinpeng Zheng, Chunyuan Mao, Yunxin Xu, Renzhe Yang, Jinxuan Chen, Yuanlong Huang, Wangrong Yang, Shaowu Yang, Wenjing Liu, Xinwang Cui, Peng Wang, Haotian |
| author_facet | Lv, Xinpeng Zheng, Chunyuan Mao, Yunxin Xu, Renzhe Yang, Jinxuan Chen, Yuanlong Huang, Wangrong Yang, Shaowu Yang, Wenjing Liu, Xinwang Cui, Peng Wang, Haotian |
| contents | Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models. Existing fairness-aware SC approaches primarily focus on group fairness and typically assume that agents respond independently. However, when individual fairness is required, ensuring similar individuals receive similar outcomes, agents' manipulation becomes interdependent: an agent's preferred manipulation depends on the neighborhoods' outcomes. This induces a mismatch between classical SC formulations and fairness-aware decision settings, where independent models no longer accurately characterize strategic manipulations. To address this issue, we introduce individual fairness-aware strategic classification (IFSC), a framework that models peer-driven manipulation arising from individual fairness, where agents imitate nearby positively decided peers to obtain favorable outcomes. IFSC characterizes strategic manipulation as similarity-based imitation toward visible accepted peers and learns classifiers under the resulting post-manipulation distributions. To account for uncertainty in peer observability, IFSC employs a robust learning process that introduces stochastic perturbations during manipulation simulation. Experiments on synthetic and real-world datasets demonstrate that IFSC improves individual-fairness consistency and mitigates imitation-induced distortions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00827 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation Lv, Xinpeng Zheng, Chunyuan Mao, Yunxin Xu, Renzhe Yang, Jinxuan Chen, Yuanlong Huang, Wangrong Yang, Shaowu Yang, Wenjing Liu, Xinwang Cui, Peng Wang, Haotian Machine Learning Artificial Intelligence Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models. Existing fairness-aware SC approaches primarily focus on group fairness and typically assume that agents respond independently. However, when individual fairness is required, ensuring similar individuals receive similar outcomes, agents' manipulation becomes interdependent: an agent's preferred manipulation depends on the neighborhoods' outcomes. This induces a mismatch between classical SC formulations and fairness-aware decision settings, where independent models no longer accurately characterize strategic manipulations. To address this issue, we introduce individual fairness-aware strategic classification (IFSC), a framework that models peer-driven manipulation arising from individual fairness, where agents imitate nearby positively decided peers to obtain favorable outcomes. IFSC characterizes strategic manipulation as similarity-based imitation toward visible accepted peers and learns classifiers under the resulting post-manipulation distributions. To account for uncertainty in peer observability, IFSC employs a robust learning process that introduces stochastic perturbations during manipulation simulation. Experiments on synthetic and real-world datasets demonstrate that IFSC improves individual-fairness consistency and mitigates imitation-induced distortions. |
| title | Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2606.00827 |