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Research paper examines disparate impact in synthetic data generation

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the concept of disparate impact in synthetic data generation (SDG), examining whether the utility of generated data is consistent across different sensitive groups. The authors propose that achieving non-disparate impact means the synthetic data distribution should match the real data distribution. The paper identifies potential failure points in SDG methods, including approximation and estimation errors that can disproportionately affect certain groups, and illustrates these issues with both artificial and real-world data. AI

IMPACT Highlights potential biases in synthetic data generation, crucial for ensuring fairness in AI model training.

RANK_REASON The cluster contains an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research paper examines disparate impact in synthetic data generation

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The cluster contains an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Paul Andrey, Micha\"el Perrot, Batiste Le Bars, Marc Tommasi ·

    Disparate Impact in Synthetic Data Generation

    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 …

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