Overview

Algorithmic auditing studies how to test automated decisions and the systems around them, especially when errors or disparities affect people. Common approaches compare subgroup outcomes, use counterfactual or causal tests, probe model behavior under perturbations, and apply statistical methods such as sequential testing and uncertainty analysis. Audits increasingly examine deployed pipelines, data and component interactions, strategic responses, and evidence trails—not just a model’s aggregate accuracy. A central challenge is making conclusions reliable and meaningful despite limited access, shifting conditions, incomplete data, and metrics that can be gamed or fail to capture the harms that matter.

Latest papers

CardsList

143 papers27 papers in the last four weeks, up 59% on the four weeks before. 0.2% of all new papers.

  1. Who Audits the Auditor? Tamper-Proof Fraud Detection with Blockchain-Anchored Explainable ML

    Apr 23, 2026Zhaohui WangData ProvenanceAlgorithmic Auditing

  2. Participatory provenance as representational auditing for AI-mediated public consultation

    Apr 22, 2026Sachit MahajanData ProvenanceAlgorithmic Auditing

  3. Fairness Audits of Institutional Risk Models in Deployed ML Pipelines

    Apr 21, 2026Kelly McConvey, Dipto Das, Maya Ghai +3Algorithmic AuditingEducational Technology

  4. Shapley Value-Guided Adaptive Ensemble Learning for Explainable Financial Fraud Detection with U.S. Regulatory Compliance Validation

    Apr 14, 2026Mohammad Nasir Uddin, Md Munna AzizFinancial Fraud DetectionEnsemble Learning

  5. AI Integrity: A New Paradigm for Verifiable AI Governance

    Apr 13, 2026Seulki LeeAI GovernanceAI Assurance

  6. Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Software

    Apr 13, 2026Angela Jin, Alexander Asemota, Dan E. Krane +2Algorithmic Auditing

  7. Why Aggregate Accuracy is Inadequate for Evaluating Fairness in Law Enforcement Facial Recognition Systems

    Mar 30, 2026Khalid Adnan AlsayedAlgorithmic FairnessGroup Fairness

  8. Synthetic Designed Experiments for Diagnosing Vision Model Failure

    Mar 30, 2026Krisanu SarkarSynthetic Data EvaluationAlgorithmic Auditing

  9. Explainability and Certification of AI-Generated Educational Assessments

    Mar 18, 2026Antoun Yaacoub, Zainab Assaghir, Anuradha KarEducational AssessmentGenerative AI in Education

  10. A multimodal slice discovery framework for systematic failure detection and explanation in medical image classification

    Feb 27, 2026Yixuan Liu, Kanwal K. Bhatia, Ahmed E. FetitAlgorithmic AuditingMedical Image Classification

  11. Global Sequential Testing for Multi-Stream Auditing

    Feb 25, 2026Beepul Bharti, Ambar Pal, Jeremias SulamSequential Hypothesis TestingMultiple Hypothesis Testing

  12. Governing AI Forgetting: Auditing for Machine Unlearning Compliance

    Feb 16, 2026Qinqi Lin, Ningning Ding, Lingjie Duan +1Algorithmic AuditingMachine Unlearning

  13. Falsifying Discriminant Validity of Predictive Algorithms

    Jan 23, 2026Amanda CostonAlgorithmic AuditingPsychometric Validation

  14. White-Box Sensitivity Auditing with Steering Vectors

    Jan 23, 2026Hannah Cyberey, Yangfeng Ji, David EvansAlgorithmic AuditingLLM Auditing

  15. A framework for auditing grounding claims

    Dec 5, 2025Daniel Quigley, Eric MaynardAlgorithmic Auditing

  16. A global log for medical AI

    Oct 5, 2025Ayush Noori, Aaron E. Boussina, Hai Ho Bich +48Algorithmic AuditingHealthcare