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Related Articles from SNS
The Fair Lending Model: How the Longest-Running Algorithmic Fairness Programs Work in Practice
arXiv:2606.02957v2 Announce Type: replace Abstract: U.S. financial institutions subject to fair lending laws have been running algorithmic fairness programs for decades. Despite this long history, remarkably little is known about how these requirements operate in practice. In this paper, we offer the first empirical account of how financial institutions test for and mitigate algorithmic discrimination on the ground.
The Fair Lending Model: How the Longest-Running Algorithmic Fairness Programs Work in Practice
arXiv:2606.02957v1 Announce Type: new Abstract: U.S. financial institutions subject to fair lending laws have been running algorithmic fairness programs for decades. Despite this long history, remarkably little is known about how these requirements operate in practice. In this paper, we offer the first empirical account of how financial institutions test for and mitigate algorithmic discrimination on the ground.
Algorithmically Fair Maximization of Multiple Submodular Objective Functions and Implications to Constrained Fair Division
Announce Type: replace Abstract: Constrained maximization of submodular functions is a central problem in combinatorial optimization. In many realistic scenarios, multiple agents each need to maximize their own submodular objective over a common ground set, subject to individual constraints, with the requirement that their solutions be disjoint. We study this setting through the lens of algorithmic fairness and constrained fair division.
Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?
Announce Type: new Abstract: Machine learning engineering (MLE) agents promise to automate end-to-end ML pipeline development from raw data and natural language instructions, potentially making ML accessible to non-technical domain experts. However, in sensitive and regulated domains, this abstraction creates a responsibility gap: end-users may lack visibility into design choices that affect correctness, robustness, fairness, and regulatory compliance.
Avoiding Structural Failure Modes in Tabular Fair SSL: Online Primal-Dual Allocation under Confidence Gating
Announce Type: replace Abstract: Semi-supervised learning (SSL) enables prediction with limited labels, but high-stakes tabular applications (medical, credit, recidivism) require statistical fairness guarantees. We identify a structural conflict in tabular fair SSL through a diagnostic stress test: under confidence-gated pseudo-labeling, moment-matching fairness regularizers can trigger two failure modes -- Masking Collapse (fairness erodes confidence, starving pseudo-labels) and Trivial...
Who has the better concert lineup, a state fair or Trump’s 250th America birthday bash?
Who has the better concert lineup, a state fair or Trump’s 250th America birthday bash? Trump says he’s canceling the whole thing and bringing in Lee Greenwood for his ‘Great American State Fair.’ Here’s what you’ll find at actual state fairs - Bookmark - CommentsGo to comments The Beach Boys.
Fairness in two-player zero-sum games with bandit feedback
Announce Type: new Abstract: We study two-player zero-sum games (TPZSGs) with bandit feedback under fairness constraints requiring every action to be played with probability at least $\alpha/m$. Existing instance-dependent results target $\textit{pure}$ Nash equilibria, while fairness generically produces $\textit{mixed}$ equilibria, a harder learning target. Our key technical tool is a reparametrization: every fair strategy decomposes as $p = (\alpha/m)\mathbf{1} + (1-\alpha)\widetilde{p}$...
UniFair: A unified fair clustering approach based on separation and compactness
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. Existing fair clustering methods typically optimize a single notion of fairness and often overlook how clustering costs interact with the geometry of the induced decision boundaries. We propose \textsc{UniFair}, a unified framework that jointly optimizes \emph{separation fairness} and...
UniFair: A unified fair clustering approach based on separation and compactness
arXiv:2606.04777v2 Announce Type: replace 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. Existing fair clustering methods typically optimize a single notion of fairness and often overlook how clustering costs interact with the geometry of the induced decision boundaries. We propose UniFair, a unified framework that jointly optimizes separation fairness and...
Beyond Procedure: Substantive Fairness in Conformal Prediction
arXiv:2602.16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored. Moving beyond CP as a standalone operation (procedural fairness), we analyze the holistic decision-making pipeline to evaluate substantive fairness-the equity of downstream outcomes. Theoretically, we derive an upper bound that decomposes...