The imperative of interpretable machines

Fairness in machine-assisted decision making is critical to consider, since a lack of fairness can harm individuals or groups, erode trust in institutions and systems, and reinforce structural discrimination. To avoid making ethical mistakes, or amplifying them, it is important to ensure that the algorithms we develop are fair and promote trust. We argue that the marriage of techniques from behavioral science and computer science is essential to develop algorithms that make ethical decisions and ensure the welfare of society. Specifically, we focus on the role of procedural justice, moral cognition, and social identity in promoting trust in algorithms and offer a road map for future research on the topic.