ICIS 2020: study regarding influence of algorithm aversion and anthropomorphic agent design on the acceptance of AI-based job recommendations
In the proceedings of the International Conference on Information Systems (ICIS) 2020, a study on the influence of algorithm aversion and anthropomorphism on the acceptance of AI-based job recommendations is published. ICIS is the most important international scientific information systems conference and is classified in the category “A” according to the ranking VHB-JOURQUAL 3.
The paper deals with developments in artificial intelligence (AI), which offer 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. 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.