A Framework for Fairer Machine Learning in Organizations

Abstract

The use of machine learning in organizations presents a double-edged sword: machine learning tools reduce costs on otherwise repetitive, time-consuming tasks, yet run the risks of introducing systematic unfairness in organizational processes. Issues of behavioral ethics in machine learning implementations in organizations have not been thoroughly addressed in prior literature, as many of the necessary concepts are disparate across three literatures – ethics, machine learning, and management. Further, tradeoffs between fairness criteria in machine learning have not been addressed with regards to organizations. We move research forward by introducing an organizing framework for selecting and implementing fair algorithms in organizations.