Many studies have framed algorithmic fairness as a mathematical problem, proposing axiomatic constraints without fully considering the objectives of an intervention. My coauthors and I devised a new approach that uses contextual bandits and convex optimization to achieve outcomes that align with policymakers’ preferences for how to make difficult tradeoffs. We demonstrate the advantages of this approach using data from the Santa Clara Public Defender in a paper in Management Science.