New Mathematical Framework for Auditing Black-Box AI Decision Systems
Researchers have developed a mathematical decomposition that allows auditing of black-box algorithmic decision-makers by analyzing the relationship between costs and policy decisions over time. The method extends previous single-period analysis to multi-period settings and provides exact formulas under specific conditions. This framework enables practical auditing of AI systems in platform design, auctions, and other sequential decision systems without requiring access to internal model details.
A new theoretical framework published on arXiv provides a method for auditing black-box algorithmic decision-makers using only observable inputs and outputs. The core contribution is an exact mathematical decomposition showing that cumulative regret equals the sum of per-period covariances between cost vectors and policy decisions, extending prior single-period work to dynamic programming settings. The researchers prove this identity holds exactly under i.i.d. costs and mean-unbiased Markov policies, and derive corrections for non-stationary cases. The framework has practical applications in platform mechanism design, repeated games, procurement, and ad auctions, where it can quantify welfare loss from strategic behavior. The associated estimator is computationally tractable with O(T·nd) time complexity and is asymptotically normal, making it suitable for real-world algorithmic auditing without requiring access to proprietary model internals.
What's missing
The paper references a 2026 work by Aldridge, which appears to be a future or fictional citation that raises questions about the document's authenticity or dating. Additionally, empirical validation results on real-world datasets or systems are not described in the abstract, limiting assessment of practical effectiveness beyond theoretical guarantees.
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- arXiv cs.AICenter
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