Artificial Intelligence and Algorithmic Bias: Source, Detection, Mitigation, and Implications
Abstract
Artificial intelligence 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 increasingly been found biased, creating and perpetuating structural inequalities in society. With the rising concerns about algorithmic bias, a growing body of literature has attempted 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.
Video of this TutORial from the 2020 INFORMS Annual Meeting, held virtually November 11, 2020, is available at https://youtu.be/JBQG4nq9tV4.
This article appears in INFORMS Analytics Collections Vol. 16: Advances in Integrating AI & O.R. Visit this collection for free access to more articles showcasing the depth and breadth of research and applications at the intersection of AI and operations research.
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