Ethical Considerations in AI-Based Recruitment

Abstract

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.

Perception of fairness in algorithmic decisions: Future developers' perspective

“…In a similar vein, while approximately 22.2% of our participants have been trained on these topics, only 14.1% selected the upper end of the scale (4-5, very knowledgeable). Despite the fact that the participants are students at undergraduate or postgraduate level, and some are PhD students, only one reported being very knowledgeable (5). Even though participants indicated they were trained they might feel that they are not knowledgeable enough in these topics or they might be underestimating of their knowledge and understanding on the topics.…”

“…Algorithmic systems are increasingly gaining an important role in decision-making, deciding on the posts and news we will see on social media 1 , 2 contributing to regulated areas, such as health, 3 prison releases 4 and job hiring, 5 even moderating education 6 and many more. The use of algorithmic decision-making has prospects to make decision-making more efficient and reliable.…”

On Exploring the Possibilities and the Limits of AI for an Interoperable and Empowering Industry 4.0

“…According to authors in [6], Industry 4.0 has embraced AI technology as one of the key enablers. At the same time, also predicting employee attrition using machine learning is not a novel idea and, in some cases, raises also ethical concerns due to the possibility to inject AI-based biases in the decision process [3]. This is not a show stop for research.…”

Recruitment Search Engines for Screening Resumes through AI by Using Boolean Search Functions

“…(Wilfred, 2018) While Artificial Intelligence helps to make recruiting tasks more effective and apparently less discriminatory by integration, it relies entirely on human-generated data, and may therefore have subconscious human bias translated to model which is embedded in AI through humans. (Mujtaba & Mahapatra, 2019) There are many successful initiatives that work to improve chances of getting a job on a worldwide scale by creating a suitable career for ideal job seekers. (Keršič, Štukelj, Kamišalić, Karakatić, & Turkanović, 2019) AI saves time, which results in not doing the repetitive tasks.…”