arXiv:2606.08791v1 Announce Type: cross Abstract: We study the problem of auditing a black-box algorithmic decision-maker from observable inputs and outputs alone. Our main result is an exact decomposition: under precisely characterized conditions, the cumulative \emph{regret} of a dynamic policy equals the sum of per-period covariances between the cost vector and the policy's decision. This extends the single-period identity of Aldridge~(2026) to the full multi-period setting of stochastic dynamic programming. We prove the identity holds exactly under i.i.d. costs and mean-unbiased Markov pol
The proliferation of black-box AI models in critical decision-making necessitates robust auditing methods to ensure transparency and accountability, particularly within investment structures.
This research provides a foundational mathematical framework for understanding and auditing the performance of black-box AI investment strategies, offering critical insights for risk management and regulatory oversight.
The ability to precisely decompose and quantify 'regret' in dynamic AI policies changes how investment firms and regulators can evaluate and manage algorithmic decision-makers.
- · AI-driven investment funds with transparent auditing
- · Quantitative risk management firms
- · Financial regulators and auditors
- · Researchers in algorithmic transparency
- · Opaque black-box AI financial models
- · Investment firms reliant on unaudited algorithms
Increased scrutiny and demand for explainable AI in financial and other high-stakes decision-making contexts.
Development of new regulatory frameworks and industry standards for AI model auditing and compliance based on these mathematical principles.
A competitive advantage for firms that integrate robust auditing into their AI development pipelines, potentially leading to more ethical and reliable AI systems across industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI