Algorithmic equity in the hiring of underrepresented IT job candidates

Purpose The purpose of this paper is to offer a critical analysis of talent acquisition software and its potential for fostering equity in the hiring process for underrepresented IT professionals. The under-representation of women, African-American and Latinx professionals in the IT workforce is a l...

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Bibliographic Details
Published in:Online information review 2020-06, Vol.44 (2), p.383-395
Main Authors: Yarger, Lynette, Cobb Payton, Fay, Neupane, Bikalpa
Format: Article
Language:English
Subjects:
African Americans
Algorithms
Artificial intelligence
Bias
Codification
Computer Software
Decision making
Decision theory
Educational Technology
Employment
Epistemology
Ethnic Groups
Females
Feminism
Gender Discrimination
Homogeneity
Justice
Labor Force Development
Mathematical Models
Meta Analysis
Minority & ethnic groups
Personnel Selection
Race
Racial Discrimination
Software
Talent
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Summary:Purpose The purpose of this paper is to offer a critical analysis of talent acquisition software and its potential for fostering equity in the hiring process for underrepresented IT professionals. The under-representation of women, African-American and Latinx professionals in the IT workforce is a longstanding issue that contributes to and is impacted by algorithmic bias. Design/methodology/approach Sources of algorithmic bias in talent acquisition software are presented. Feminist design thinking is presented as a theoretical lens for mitigating algorithmic bias. Findings Data are just one tool for recruiters to use; human expertise is still necessary. Even well-intentioned algorithms are not neutral and should be audited for morally and legally unacceptable decisions. Feminist design thinking provides a theoretical framework for considering equity in the hiring decisions made by talent acquisition systems and their users. Social implications This research implies that algorithms may serve to codify deep-seated biases, making IT work environments just as homogeneous as they are currently. If bias exists in talent acquisition software, the potential for propagating inequity and harm is far more significant and widespread due to the homogeneity of the specialists creating artificial intelligence (AI) systems. Originality/value This work uses equity as a central concept for considering algorithmic bias in talent acquisition. Feminist design thinking provides a framework for fostering a richer understanding of what fairness means and evaluating how AI software might impact marginalized populations.
ISSN:1468-4527
1468-4535
DOI:10.1108/OIR-10-2018-0334