arXiv:2605.25114v1 Announce Type: cross Abstract: Reinforcement learning algorithms are generally designed to maximize the expected return across a population. However, a policy that is optimal on average may be suboptimal for certain individuals, leading to potential safety concerns. To address this, we first formalize the notion of individual harm from a counterfactual perspective and define harm as the event in which a chosen action results in a strictly worse outcome than a baseline alternative. We then propose a general two-stage procedure for learning policies that maximize the expected
The increasing deployment of AI in high-stakes domains necessitates robust safety measures, driving research into counterfactual harm to ensure responsible development.
This research addresses a critical limitation in current reinforcement learning, moving beyond average-case optimization to individual safety, which is paramount for ethical and reliable AI systems.
The formalization of 'individual harm' and a proposed two-stage learning procedure could lead to the development of AI that prioritizes individual safety over population-level optimization, influencing future AI ethics and regulation.
- · AI safety researchers
- · Developers of high-stakes AI systems
- · Users of AI in sensitive applications
- · AI systems prioritizing pure efficiency over safety
- · Organizations deploying AI without safety considerations
AI models will incorporate explicit mechanisms to prevent individual harm, leading to more trustworthy systems.
This could set new standards for regulatory compliance and certification of AI, especially in fields like healthcare or autonomous vehicles.
Public confidence in AI could increase significantly, accelerating adoption in areas previously deemed too risky due to individual harm concerns.
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Read at arXiv cs.LG