Auditing Black-Box Prediction Models for Data Minimization Compliance
(NeurIPS) Measures the level of data minimization satisfied by the prediction model using a limited number of queries.
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- 2026-10-10
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Auditing Algorithms: On Lessons Learned and the Risks of DataMinimization dl.acm.org
(AIES) A practical audit for a well-being recommendation app developed by Telefónica (mostly on bias).
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SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency arxiv.org
(ICLR) Considers backdoor detection under the black-box setting in machine learning as a service (MLaaS) applications.
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Auditing Black-Box Models for Indirect Influence arxiv.org
(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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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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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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Stealing Machine Learning Models via Prediction APIs usenix.org
(Usenix Security) (Code) Aims at extracting machine learning models in use by remote services.
Audit Algorithms › Papers > 2021: “(NeurIPS) Measures the level of data minimization satisfied by the prediction model using a limited number of queries.”