Many studies have framed algorithmic fairness as a mathematical problem, proposing axiomatic constraints without fully considering the consequences of these constraints. My coauthors and I devised a new approach that uses contextual bandits and convex optimization to achieve equitable outcomes. We demonstrate the advantages of this approach using data from the Santa Clara Public Defender in a paper in Management Science.