Algorithmic recommendations and decisions
have become ubiquitous in today's society. Many of these and other
data-driven policies are based on known, deterministic rules to
ensure their transparency and interpretability. This is especially
true when such policies are used for public policy decision-making.
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. Unfortunately, existing methods for policy
learning are not applicable because they require existing policies
to be stochastic rather than deterministic. We develop a robust
optimization approach that partially identifies the expected utility
of a policy, and then finds an optimal policy by minimizing the
worst-case regret. The resulting policy is conservative but has a
statistical safety guarantee, allowing the policy-maker to limit the
probability of producing a worse outcome than the existing policy.
We extend this approach to common and important settings where
humans make decisions with the aid of algorithmic recommendations.
Lastly, we apply the proposed methodology to a unique field
experiment on pre-trial risk assessments. We derive new
classification and recommendation rules that retain the transparency
and interpretability of the existing risk assessment instrument
while potentially leading to better overall outcomes at a lower
cost. (Last updated in September 2021)