AI and Algorithmic Bias: Source, Detection, Mitigation and Implications

Abstract

Artificial intelligence (AI) and machine learning (ML) algorithms are widely used throughout our economy in making decisions that have far-reaching impacts on employment, education, access to credit, and other areas. Initially considered neutral and fair, ML algorithms have recently been found increasingly biased, creating and perpetuating structural inequalities in society. With the rising concerns about algorithmic bias, a growing body of literature attempts to understand and resolve the issue of algorithmic bias. In this tutorial, we discuss five important aspects of algorithmic bias. We start with its definition and the notions of fairness policy makers, practitioners, and academic researchers have used and proposed. Next, we note the challenges in identifying and detecting algorithmic bias given the observed decision outcome, and we describe methods for bias detection. We then explain the potential sources of algorithmic bias and review several bias-correction methods. Finally, we discuss how agents’ strategic behavior may lead to biased societal outcomes, even when the algorithm itself is unbiased. We conclude by discussing open questions and future research directions.

Keywords: artificial intelligence; machine learning; fair machine learning; algorithms; unintended consequence of algorithms; algorithmic bias; algorithmic transparency

JEL Classification: C00, M1, M00