Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems

Understand

In the long term, reinforcement learning (RL) is considered by many AI theorists to be the most promising path to artificial general intelligence.

• This places RL practitioners in a position to design systems that have never existed before and lack prior documentation in law and policy.
• Public agencies could intervene on complex dynamics that were previously too opaque to deliberate about, and long-held policy ambitions would finally be made tractable.
• In this whitepaper we illustrate this potential and how it might be technically enacted in the domains of energy infrastructure, social media recommender systems, and transportation.