Detecting racial bias in algorithms and machine learning

Abstract

Purpose

The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of unambiguous instructions, bias is a byproduct of these computations, bringing harm to historically disadvantaged populations. This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Relying upon sociological and technical research, the paper offers commentary on the need for more workplace diversity within high-tech industries and public policies that can detect or reduce the likelihood of racial bias in algorithmic design and execution.

Design/methodology/approach

The paper shares examples in the US where algorithmic biases have been reported and the strategies for explaining and addressing them.

Findings

The findings of the paper suggest that explicit racial bias in algorithms can be mitigated by existing laws, including those governing housing, employment, and the extension of credit. Implicit, or unconscious, biases are harder to redress without more diverse workplaces and public policies that have an approach to bias detection and mitigation.

Research limitations/implications

The major implication of this research is that further research needs to be done. Increasing the scholarly research in this area will be a major contribution in understanding how emerging technologies are creating disparate and unfair treatment for certain populations.

Practical implications

The practical implications of the work point to areas within industries and the government that can tackle the question of algorithmic bias, fairness and accountability, especially African-Americans.

Social implications

The social implications are that emerging technologies are not devoid of societal influences that constantly define positions of power, values, and norms.

Originality/value

The paper joins a scarcity of existing research, especially in the area that intersects race and algorithmic development.

Citation

Turner Lee, N. (2018), "Detecting racial bias in algorithms and machine learning", Journal of Information, Communication and Ethics in Society, Vol. 16 No. 3, pp. 252-260. https://doi.org/10.1108/JICES-06-2018-0056

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