Ethical Considerations in AI-Based Recruitment

By Dena F. Mujtaba, Nihar Ranjan Mahapatra. 2019

112 citations recorded by the source corpus.

Over the past few years, machine learning and AI have become increasingly common in human resources (HR) applications, such as candidate screening, resume parsing, and employee attrition and turnover prediction. Though AI assists in making these tasks more efficient, and seemingly less biased through automation, it relies heavily on data created by humans, and consequently can have human biases carry over to decisions made by a model. Several studies have shown biases in machine learning applications such as facial recognition and candidate ranking. This has spurred active research on the topic of fairness in machine learning over the last five years. Several toolkits to mitigate biases and interpret black box models have been developed in an effort to promote fair algorithms. This paper presents an overview of fairness definitions, methods, and tools as they relate to recruitment and establishes ethical considerations in the use of machine learning in the hiring space.

Subfields: Health Informatics, Organizational Behavior and Human Resource Management, Safety Research, Safety, Robustness & Interpretability

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Citations in the corpus

Ethical and social risks of harm from Language Models (2021, background): “…caused by discriminatory systems is particularly salient if bias occurs in applications that materially impact people’s lives, such as predicting a person’s creditworthiness (Mehrabi et al., 2019), criminal recidivism (Angwin et al., 2016), or suitability to a job (Mujtaba and Mahapatra, 2019).”