Algorithmic Bias Detection and Mitigation: Best Practices and Policies to Reduce Consumer Harms

Author: Nicole Turner Lee, Paul Resnick, Genie Barton

Publisher: Brookings

Publication Year: 2019

Summary: The following article introduces a framework for algorithmic hygiene which lists specific causes of bias with best practices on how to recognize these biases and eliminate them. The paper also includes a questions template for identifying the impact of bias whenever you start a data-driven project. The questions template had these main questions: 1). What will the automated decision do; 2). How will potential bias be detected; 3). What are the operator incentives; 4). How are the stakeholders being engaged; and 5). Has diversity been considered in the design and execution?