Open Internet by MindsNet

Evaluating AI Decision Quality Under Uncertainty

Current methods for judging AI decision-making quality, such as returns, are flawed in uncertain environments. A good decision can result in a loss due to uncontrollable factors, while a bad decision can be lucky. There is a need for new evaluation methods that separate decision quality from outcomes.

Computing & Technology, Computer Science, Machine Learning