arXiv AI July 9, 2026 By Joseph Bingham, Netanel Arussy, Dvir Aran

SOMtime the World Ain$'$t Fair: Violating Fairness Using Self-Organizing Maps

arXiv:2602. 18201v2 Announce Type: replace Abstract: Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 10

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.

By Mina Arzaghi, Alireza Dehghanpour Farashah, Florian Carichon, Jean-Fran\c{c}ois Plante, Golnoosh Farnadi

llms rag fine-tuning benchmarks safety

arXiv Machine Learning

Jun 4

UniFair: A unified fair clustering approach based on separation and compactness

arXiv:2606. 04777v1 Announce Type: new Abstract: Clustering is increasingly used to support high-impact decisions, yet standard objectives such as $k$-means can produce clusterings that treat demographic groups unequally.

By Antonia Karra, Vasiliki Papanikou, Georgios Vardakas, Evaggelia Pitoura, Aristidis Likas

diffusion safety

arXiv Machine Learning

Aug 11

Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study

arXiv:2608. 09899v1 Announce Type: new Abstract: In fair ranked link prediction, demographic parity ($\Delta_\mathrm{DP}$) is a common fairness metric.

By Valentijn Oldenburg, Floris de Kam, Stef de Wildt, Jarno Nilson Balk

safety

arXiv Machine Learning

Jul 27

Unbiased Open World Regularization for Fair Self-Supervised Learning

arXiv:2607. 22149v1 Announce Type: new Abstract: Despite recent advances, self-supervised learning (SSL) models and Joint-Embedding Predictive Architectures (JEPAs) remain susceptible to learning spurious biases in the dataset.

By L{\'e}o Nicollier (CB, ATT), Marc Pic (ATT), Pablo Mus{\'e} (CB, IFUMI), Enric Meinhardt-Llopis (CB), Gabriele Facciolo (CB)

rag diffusion benchmarks safety

arXiv Machine Learning

Jun 19

Data Bias Mitigation under Coverage Constraints & The Price of Fairness

arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.

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

safety

arXiv AI

Jul 24

Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning

arXiv:2607. 21300v1 Announce Type: cross Abstract: Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations.

By Lorenzo Orsingher, Thomas De Min, Massimiliano Mancini, Davide Talon, Elisa Ricci

llms nlp fine-tuning multimodal benchmarks safety

Related stories

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

UniFair: A unified fair clustering approach based on separation and compactness

Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study

Unbiased Open World Regularization for Fair Self-Supervised Learning

Data Bias Mitigation under Coverage Constraints & The Price of Fairness

Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning