  The recruitment process is crucial to an organization's ability to position
itself for success, from finding qualified and well-fitting job candidates to
impacting its output and culture. Therefore, over the past century, human
resources experts and industrial-organizational psychologists have established
hiring practices such as attracting candidates with job ads, gauging a
candidate's skills with assessments, and using interview questions to assess
organizational fit. However, the advent of big data and machine learning has
led to a rapid transformation in the traditional recruitment process as many
organizations have moved to using artificial intelligence (AI). Given the
prevalence of AI-based recruitment, there is growing concern that human biases
may carry over to decisions made by these systems, which can amplify the effect
through systematic application. Empirical studies have identified prevalent
biases in candidate ranking software and chatbot interactions, catalyzing a
growing body of research dedicated to AI fairness over the last decade. This
paper provides a comprehensive overview of this emerging field by discussing
the types of biases encountered in AI-driven recruitment, exploring various
fairness metrics and mitigation methods, and examining tools for auditing these
systems. We highlight current challenges and outline future directions for
developing fair AI recruitment applications, ensuring equitable candidate
treatment and enhancing organizational outcomes.
