Leveraging Imperfect Sources to Detect Fairwashing in Black-Box Auditing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866915867596423168 |
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| author | Bourrée, Jade Garcia Merrer, Erwan Le Tredan, Gilles Rottembourg, Benoît |
| author_facet | Bourrée, Jade Garcia Merrer, Erwan Le Tredan, Gilles Rottembourg, Benoît |
| contents | Algorithmic auditing has become central to platform accountability under frameworks such as the AI Act and the Digital Services Act. In practice, this obligation is discharged through dedicated Audit APIs. This architecture creates a paradox: the entity under scrutiny controls the evaluation interface. A platform facing legal sanctions can serve a compliant surrogate model on its Audit API, while running a discriminatory production system. This deceptive practice is known as fairwashing. Manipulation is undetectable if the auditor relies on only one source. To address this limitation, we introduce the Two-Source Audit Model (2SAM). This model cross-references the Audit API with an independent trusted stream. The key insight is that the trusted stream does not need to be perfectly aligned with the Audit API. We introduce a consistency proxy, a probabilistic mapping that can reconcile discrepancies between sources. This approach yields three results. First, we quantify the rate of manipulation above which a single-source auditor is blind. Second, we show how proxy quality governs detection power. Third, we provide a closed-form budget condition guaranteeing detection at any target confidence level, closing the blind spot mentioned above. We validate 2SAM on the UCI Adult dataset, achieving $70\%$ detection power with as few as $127$ cross-verification queries out of a total budget of $750$, using a name-based gender proxy with $94.2\%$ accuracy. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_13883 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Leveraging Imperfect Sources to Detect Fairwashing in Black-Box Auditing Bourrée, Jade Garcia Merrer, Erwan Le Tredan, Gilles Rottembourg, Benoît Machine Learning Computers and Society Software Engineering Algorithmic auditing has become central to platform accountability under frameworks such as the AI Act and the Digital Services Act. In practice, this obligation is discharged through dedicated Audit APIs. This architecture creates a paradox: the entity under scrutiny controls the evaluation interface. A platform facing legal sanctions can serve a compliant surrogate model on its Audit API, while running a discriminatory production system. This deceptive practice is known as fairwashing. Manipulation is undetectable if the auditor relies on only one source. To address this limitation, we introduce the Two-Source Audit Model (2SAM). This model cross-references the Audit API with an independent trusted stream. The key insight is that the trusted stream does not need to be perfectly aligned with the Audit API. We introduce a consistency proxy, a probabilistic mapping that can reconcile discrepancies between sources. This approach yields three results. First, we quantify the rate of manipulation above which a single-source auditor is blind. Second, we show how proxy quality governs detection power. Third, we provide a closed-form budget condition guaranteeing detection at any target confidence level, closing the blind spot mentioned above. We validate 2SAM on the UCI Adult dataset, achieving $70\%$ detection power with as few as $127$ cross-verification queries out of a total budget of $750$, using a name-based gender proxy with $94.2\%$ accuracy. |
| title | Leveraging Imperfect Sources to Detect Fairwashing in Black-Box Auditing |
| topic | Machine Learning Computers and Society Software Engineering |
| url | https://arxiv.org/abs/2305.13883 |