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Evidence record 14460 · automatically gathered

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data

Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness. Differential Privacy (DP) has become a gold standard for privacy-preserving data analysis, while fairness-aware mechanisms aim to mitigate discrimination against underrepresented groups. However, these objectives can conflict: DP often amplifies disparities across demographic groups, and little is known about whether established fairness interventions remain effective under

Record details

Published: 8 July 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Bias & fairness · Privacy
Retrieved: 29 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Bias & fairness

Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data

arXiv · 8 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

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Shared topic: Bias & fairness · Shared topic: Privacy

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Shared topic: Bias & fairness · Shared topic: Privacy

TIER: Trajectory-Invariant Explanation Regularization for Membership Privacy

arXiv · 3 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithms

arXiv · 16 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning

arXiv cs.CR (AI security) · 18 July 2026

How to cite this record

ethics.ai (8 July 2026), “Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data,” evidence record 14460, https://ethics.ai/record/14460 (originally published by arXiv cs.CR (AI security)).

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