arXiv Machine Learning June 16, 2026 By Tianhe Zhang, Suhan Liu, Peng Shi

Discrimination-free Insurance Pricing with Privatized Sensitive Attributes

Read the original on arXiv Machine Learning →

arXiv:2504. 11775v3 Announce Type: replace-cross Abstract: Fairness has become an important concern in insurance pricing as insurers increasingly rely on machine learning models to predict expected losses.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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Jul 17

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

arXiv:2607. 14607v1 Announce Type: cross Abstract: Machine learning (ML) models deployed in sensitive domains such as healthcare, law enforcement, and finance must satisfy not only utility requirements but also fairness and privacy guarantees.

By Umid Suleymanov, Ilhama Novruzova, Khalid Mammadov, Natavan Hasanova, Murat Kantarcioglu

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Jul 21

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

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Jun 24

A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning

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Jul 9

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

arXiv:2607. 07471v1 Announce Type: cross Abstract: Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness.

By Vin\'icius Gabriel Angelozzi, H\'eber H. Arcolezi

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arXiv Machine Learning

Jun 19

Data Bias Mitigation under Coverage Constraints & The Price of Fairness

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By Bruno Scarone, Alfredo Viola, Ren\'ee J. Miller

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