We Need Fairness and Explainability in Algorithmic Hiring.

Algorithms and machine learning models, including the decisions made by these models, are becoming ubiquitous in our daily life, including hiring. We make no value judgment regarding this development; rather, we simply acknowledge that it is quickly becoming reality that automation plays a role in hiring. Increasingly, these technologies are used in all of the small decisions that make up the modern hiring pipeline: from which resumes get selected for a first screen to who gets an on site interview. Thus, these algorithms and models may potentially amplify bias and (un)fairness issues for many historically marginalized groups. While there is a rapidly expanding literature on algorithmic decision making and fairness, there has been limited work on fairness specifically for online, multi-stakeholder decision making processes such as those found in hiring. We outline broad challenges including formulating definitions for fair treatment and fair outcomes in hiring, and incorporating these definitions into the algorithms and processes that constitute the modern hiring pipeline. We see the AAMAS community as uniquely positioned to address these challenges.