Fairness in Algorithmic Decision Making

Algorithmic (data-driven) decision making is increasingly being used to assist or replace human decision making in domains with high societal impact, such as banking (estimating creditworthiness), recruiting (ranking applicants), judiciary (offender profiling) and journalism (recommending news-stories). Consequently, in recent times, multiple research works have attempted to identify (measure) bias or unfairness in algorithmic decisions and propose mechanisms to control (mitigate) such biases. In this tutorial, we introduce the related literature to the cods-comad community. Moreover, going over the more prevalent works on fairness in classification or regression tasks, we explore fairness issues in decision making scenarios, where we need to account for preferences of multiple stakeholders. Specifically, we cover our own past and ongoing works on fairness in recommendation and matching systems. We discuss the notions of fairness in these contexts and propose techniques to achieve them. Additionally, we briefly touch upon the possibility of utilizing user interface of platforms (choice architecture) to achieve fair outcomes in certain scenarios. We conclude the tutorial with a list of open questions and directions for future work.