arXiv AI July 17, 2026 By Umid Suleymanov, Ilhama Novruzova, Khalid Mammadov, Natavan Hasanova, Murat Kantarcioglu

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.

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 AI.

safety

arXiv AI

Jul 21

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

arXiv:2607. 16620v1 Announce Type: cross Abstract: Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems.

By Rakshit Naidu

nlp benchmarks safety

arXiv Machine Learning

Sep 1

Revisiting the Provable-Auditable Privacy Gap of DP-SGD

arXiv:2608.28934v1 Announce Type: new Abstract: Differential privacy (DP) has traditionally been used to provide theoretical upper bounds on an algorithm's stability to changing its training data. In...

By Saloni Modi, Srivi Balaji, Yusong Zhu, Gautam Kamath, Kevin Tian

benchmarks safety

arXiv AI

3d ago

Efficient Active Auditing of Multi-Group Fairness with Bias Probes

arXiv:2609.40034v1 Announce Type: cross Abstract: Over the past decade, Machine Learning (ML) has been trained under dual objectives: minimizing prediction error via Empirical Risk Minimization (ERM)...

By Ayoub Ajarra, Debabrota Basu

safety

arXiv Machine Learning

Jun 18

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

arXiv:2502. 17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks.

By Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

safety

arXiv Machine Learning

Jun 5

Detectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run

arXiv:2605. 27292v2 Announce Type: replace Abstract: Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bounds on differential privacy (DP) parameters.

By Mathieu Dagr\'eou, Aur\'elien Bellet

rag safety

arXiv Machine Learning

Aug 6

Manipulation-Proof Oblivious Audits against Deceptive Model Providers

arXiv:2608. 04365v1 Announce Type: new Abstract: Audits have emerged as a critical instrument for algorithmic governance, providing a mechanism for external scrutiny and governance of machine learning models.

By Augustin Godinot, Sofiane Azogagh, Julien Ferry, S\'ebastien Gambs

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

Revisiting the Provable-Auditable Privacy Gap of DP-SGD

Efficient Active Auditing of Multi-Group Fairness with Bias Probes

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

Detectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run

Manipulation-Proof Oblivious Audits against Deceptive Model Providers