Algorithmic equity in the hiring of underrepresented IT job candidates

Abstract

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.

Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

“…We found that no further articles can be included in the literature review by reading the full text. Since out of these eight articles, three articles were already included in the literature review (Lee 2018;Tambe et al 2019;Yarger et al 2019), two articles were excluded in the eligibility stage of the initial search process (Hoffmann 2019; Sumser 2017) (no reference to HRM and comment), and the remaining three articles neither discussed fairness nor the HR recruitment and/or HR development context (Varghese et al 1988; Horton 2017; Gil-Lafuente and Oh 2012). The robustness check verified that the literature review offers a reliable and transparent picture of the current literature regarding the discrimination potential and fairness when using algorithmic decisionmaking in HR recruitment and HR development.…”

“…One possibility for using algorithmic decision-making in selection is the analysis of the CV and résumé, with candidates entering their CVs or job preferences online, and this information is subject to algorithmic analysis (Savage and Bales 2017). Yarger et al (2019) conceptually analyzed the fairness of talent acquisition software in the USA and its potential to promote fairness in the selection process for underrepresented IT professionals. The authors argue that it is necessary to audit algorithms, because they are not neutral.…”

“…One prominent example is the CV screening tool of Amazon, which was trained on biased historical data that led to a preference for male candidates based on the fact that, in the past, Amazon hired more often males as software engineers as females and the algorithm has been trained based on these data (historical bias) (Dastin 2018). Yarger et al (2019) suggest removing sources of human bias such as gender, race, ethnicity, religion, sexual orientation, age, and information that can indicate membership in a protected class. Text mining is often the foundation for the screening of CVs and résumés, an approach to characterize and transform text using the words themselves as the unit of analysis (e.g., the presence or absence of a specific word of interest) (Dreisbach et al 2019).…”

“…Companies should implement a data quality control process to develop quality metrics, collect new data, evaluate data quality, and remove inaccurate data from the training data set. For example, for CV and résumé screening, companies could apply blind hiring, which means removing personally identifiable information on the documents (Yarger et al 2019;Raghavan et al 2020).…”

“…Following the company's standards mentioned above, HR managers should try to get detailed information about the data sets, the codes, and the procedures and measures of the service provider to prevent biases. Furthermore, HR managers should discuss multiple options that can reduce bias, such as weighing or removing certain indicators that highly correlate with attributes (Yarger et al 2019).…”