The influence of algorithm aversion and anthropomorphic agent design on the acceptance of AI-based job recommendations

Artificial intelligence (AI) offers promising tools to support the job-seeking process by providing automatic and user-centered job recommendations. However, job seekers often hesitate to trust AI-based recommendations in this context given the far-reaching consequences of the importance of the decision for a job on their future career and life. This hesitation is largely driven by a lack of explainability, as underlying algorithms are complex and not clear to the user. Prior research suggests that anthropomorphization (i.e., the attribution of human traits) can increase the acceptance of technology. Therefore, we adapted this concept for AI-based recommender systems and conducted a survey-based study with 120 participants. We find that that using an anthropomorphic design in a recommender system for open positions increases job seekers' acceptance of the underlying system. However, algorithm aversion rises if detailed information on the algorithmic origin is being disclosed.