Gaming the Metric, Not the Harm: Certifying Safety Audits against Strategic Platform Manipulation
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Abstract (arXiv)
Online-safety regulation under the UK Online Safety Act and the EU Digital Services Act increasingly treats scalar metrics as compliance evidence. Once announced, such a metric also becomes an optimization target: a strategic platform can improve its score by routing recommendations through semantically equivalent content variants, without reducing true harm. We ask when such an audit metric can still certify a genuine reduction in harm. The protocol is modeled as a published transformation graph whose connected components form semantic classes, and the metric itself is treated as a security object. Three results follow. First, any metric that scores variants directly is manipulable as soon as two equivalent variants in a harmful class disagree in score. Second, the semantic-envelope lift, which assigns each variant the maximum score in its class, is the unique pointwise minimum among conservative classwise-constant repairs. Third, a class-stratified certificate, H^star(x) ≤ (1/hatα) MEnv(m)(x) + barη, holds for every platform strategy, with barη absorbing annotation and protocol error. We check the claims at three levels: exhaustive enumeration on a finite-state grid of mixed strategies, an SMT encoding in Z3 cross-replayed in cvc5, and a bounded single-player MDP encoded in PRISM-games. The fragile metric fails manipulation invariance and cannot support the same useful predeclared class-coverage certificate; under the envelope-level certificate, it produces large violations at every tested instance, with a large mean gaming gap across random catalogs at a fixed audit budget. The semantic-envelope metric exhibits no such violation in the tested instances.
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References · 13
- 2020Performative Predictioncited by 318
- 2015Strategic Classificationcited by 256
- 2020HateCheck: Functional Tests for Hate Speech Detection Modelscited by 215
- 2022Defining and Characterizing Reward Hackingcited by 184
- 2024Evaluations of Machine Learning Privacy Defenses are Misleadingcited by 35
- 2025Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemblecited by 11
- 2025The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandatescited by 7
- 2023The Amplification Paradox in Recommender Systemscited by 6
- 2024Harm Mitigation in Recommender Systems under User Preference Dynamicscited by 5
- 2023Auditing Recommender Systems -- Putting the DSA into practice with a risk-scenario-based approachcited by 4
- 2025You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Controlcited by 3
- 2026With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systemscited by 2
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