Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment. (arXiv:2109.11679v3 [stat.ML] UPDATED)

Algorithmic recommendations and decisions have become ubiquitous in today's
society. Many of these and other data-driven policies, especially in the realm
of public policy, are based on known, deterministic rules to ensure their
transparency and interpretability. For example, algorithmic pre-trial risk
assessments, which serve as our motivating application, provide relatively
simple, deterministic classification scores and recommendations to help judges
make release decisions. How can we use the data based on existing deterministic
policies to learn new and better policies? Unfortunately, …

application arxiv learning ml policy risk risk assessment