• Corpus ID: 246822961

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

@article{Gilbert2022ChoicesRA,
  title={Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems},
  author={Thomas Krendl Gilbert and Sarah Dean and Tom O. Zick and Nathan Lambert},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.05716}
}
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… 
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