Auditing Black-Box Models for Indirect Influence
(ICDM) Evaluate the influence of a variable on a black-box model by "cleverly" removing it from the dataset and looking at the accuracy gap
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- 2026-10-10
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Auditing fairness under unawareness through counterfactual reasoning sciencedirect.com
(Information Processing & Management) Shows how to unveil whether a black-box model, complying with the regulations, is still biased or not.
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Data driven exploratory attacks on black box classifiers in adversarial domains sciencedirect.com
(Neurocomputing) Reverse engineers remote classifier models (e.g., for evading a CAPTCHA test).
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Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation arxiv.org
(AIES) Treats black box models as teachers, training transparent student models to mimic the risk scores assigned by black-box models.
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Under manipulations, are some AI models harder to audit? grodino.github.io
(SATML) Relates the difficulty of black-box audits to the capacity of the targeted models, using the Rademacher complexity.
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Identifying the Machine Learning Family from Black-Box Models rd.springer.com
(CAEPIA) Determines which kind of machine learning model is behind the returned predictions.
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xGEMs: Generating Examplars to Explain Black-Box Models arxiv.org
(arXiv) Searches bias in the black box model by training an unsupervised implicit generative model. Thensummarizes the black-box model behavior quantitatively by perturbing data samples along the data manifold.
Audit Algorithms › Papers > 2016: “(ICDM) Evaluate the influence of a variable on a black-box model by "cleverly" removing it from the dataset and looking at the accuracy gap”