Algorithmic Auditing

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15 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems
Apr 20, 2026Junyi Yao, Zihao Zheng, Jiayu LongAlgorithmic Auditing Learning to Rank
Washington University in St. Louis USA
Falsifying Discriminant Validity of Predictive Algorithms
Jan 23, 2026Amanda CostonAlgorithmic Auditing Psychometric Validation
Statistics Department University of California, Berkeley Berkeley, CA, USA
White-Box Sensitivity Auditing with Steering Vectors
Jan 23, 2026Hannah Cyberey, Yangfeng Ji, David EvansAlgorithmic Auditing LLM Auditing
University of Virginia
A framework for auditing grounding claims
Dec 5, 2025Daniel Quigley, Eric MaynardAlgorithmic Auditing
Center for Possible Minds Indiana University Bloomington Bloomington, IN 47408 · Eruditis Milwaukee, WI 53207
A global log for medical AI
Oct 5, 2025Ayush Noori, Aaron E. Boussina, Hai Ho Bich +48Algorithmic Auditing Healthcare
How Reliable are Fairness Audits with Unreliable Data?
Jun 28, 2025Yash Vardhan TomarAlgorithmic Auditing Algorithmic Fairness
Purdue University

Apr 20, 2026, cs.LGJ/K move · Enter open · S save

Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems

Junyi Yao, Zihao Zheng, Jiayu Long

Washington University in St. Louis USA

Algorithmic Auditing Learning to Rank Adversarial Attacks Pairwise Ranking Adversarial Perturbations

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Maximum-likelihood pairwise ranking is a com- mon computational mechanism for prioritization, reputation estimation, and comparison-driven decision support. Despite its broad use, the perturbation sensitivity of this estimator under structured changes in comparison data remains insufficiently characterized. We study this question as an applied-mathematics and computational-science problem in stability analysis. We for- mulate coordinated perturbation as a budgeted subset-selection problem over pairwise observations and introduce an Adaptive Subset Selection Attack (ASSA) as a scalable search heuristic for probing high-impact perturbation sets. Through experiments on synthetic and observed preference datasets, we show that MLE-based ranking can exhibit pronounced regime-dependent sensitivity: relatively small but coordinated perturbations may in- duce meaningful changes in output orderings, while the response profile varies across budgets and data conditions. By comparing ASSA with random, greedy, and randomized subset baselines under repeated trials, we characterize both the magnitude and the variability of perturbation-induced ranking shifts. These results position pairwise ranking sensitivity as a problem in computational reliability, numerical stability, and robustness auditing for engineering systems built on comparison-driven inference.

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Latest papers 96

Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems

Falsifying Discriminant Validity of Predictive Algorithms

White-Box Sensitivity Auditing with Steering Vectors

A framework for auditing grounding claims

A global log for medical AI

How Reliable are Fairness Audits with Unreliable Data?