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

35 Pages Posted: 16 Oct 2020

See all articles by Runshan Fu

Runshan Fu

Carnegie Mellon University

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business

Param Vir Singh

Carnegie Mellon University - David A. Tepper School of Business

Date Written: July 26, 2020

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

Suggested Citation

Fu, Runshan and Huang, Yan and Singh, Param Vir, AI and Algorithmic Bias: Source, Detection, Mitigation and Implications (July 26, 2020). Available at SSRN: https://ssrn.com/abstract=3681517 or http://dx.doi.org/10.2139/ssrn.3681517

Runshan Fu

Carnegie Mellon University ( email )

5000 Forbes Ave.
Pittsburgh, PA 15213-3890
United States

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Param Vir Singh (Contact Author)

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
412-268-3585 (Phone)

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