arXiv:2507.20068v2 Announce Type: replace Abstract: Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment. Recent advances have shown that leveraging auxiliary datasets, such as those synthesized by generative models, can improve the accuracy of OPE methods. Unfortunately, such auxiliary datasets may also be biased, and existing methods for using data augmentation within OPE lack principled uncertainty quantification. In high stakes domains like healthcare, reliable uncertainty estimates are important for ensuring safe and inform
The increasing deployment of AI in high-stakes environments necessitates more robust and reliable evaluation methods, particularly as generative models produce potentially biased data for training.
Improved off-policy evaluation with principled uncertainty quantification can lead to safer and more effective AI deployments, especially in critical sectors like healthcare, accelerating trust and adoption.
The ability to accurately and reliably evaluate new AI policies before deployment, even with biased auxiliary data, changes the risk profile and development cycle for advanced AI systems.
- · Healthcare AI developers
- · Reinforcement learning researchers
- · AI safety and ethics organizations
- · Generative AI companies
- · AI systems with poor or unquantified uncertainty estimates
- · Developers relying solely on limited on-policy data
More reliable AI evaluation methods will accelerate the responsible deployment of complex AI systems in critical domains.
Increased trust in AI performance estimates could lead to broader regulatory acceptance and faster market adoption of AI solutions.
The demand for high-quality, auditable AI evaluation tools will rise, fostering new businesses specializing in AI assurance and validation.
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