English(EN) Disparate Impact in Synthetic Data Generation

研究论文探讨合成数据生成中的差异化影响

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

一篇新研究论文探讨了合成数据生成（SDG）中的差异化影响概念，考察了生成数据的效用在不同敏感群体之间是否一致。作者提出，实现非差异化影响意味着合成数据分布应与真实数据分布相匹配。该论文识别了SDG方法中潜在的故障点，包括可能不成比例地影响某些群体的近似和估计误差，并通过人工和真实世界数据来说明这些问题。 AI

影响强调了合成数据生成中潜在的偏见，这对于确保AI模型训练的公平性至关重要。

排序理由该集群包含一篇在arXiv上发表的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Newsworthiness bucket: Tool; 该集群包含一篇在arXiv上发表的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Paul Andrey, Micha\"el Perrot, Batiste Le Bars, Marc Tommasi · 2026-09-15 04:00

合成数据生成中的差异化影响

arXiv:2606.13105v2 Announce Type: replace Abstract: We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups. Our approach departs from existing work on fair …

本文如何被排名

报道来源 [1]

合成数据生成中的差异化影响

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