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AI Research
arXiv CS.AI·Jun 9·5 min read
Evaluating AI Investment Strategies
TL;DR
Auditing black-box algorithmic decision-makers has been challenging due to limited visibility into their internal workings. This study introduces an exact decomposition of cumulative regret, linking it to per-period covariances between cost vectors and policy decisions.
✦ Why It Matters
Engineers can use this framework to better evaluate and optimize dynamic decision-making algorithms in their applications.
Key Takeaways
- Utilize covariance decomposition for auditing algorithmic decisions.
- Apply bias corrections for non-stationary cost scenarios.
- Leverage the framework for platform mechanism design.
- Implement the method in ad auctions to quantify welfare loss.
How It Works
The method decomposes cumulative regret into covariances, allowing for a detailed analysis of decision-making processes. By establishing a connection to reinforcement learning through Bellman recursion, it provides a systematic approach to auditing that is both model-free and efficient.