A Trustworthy and Explainable AI Recommender System: Job Domain Case Study

Finding a job these days is challenging because of the size, diversity, and goals of the market in a society impacted by pandemics, economic crises, or military hostilities. Trust is the most crucial factor in the job domain after performance expectations. It is particularly significant for women, less active job seekers, and people who did not experience job recommendations. Since recommender systems (RS) are one of the most frequently encountered human-centered and online applications in our daily lives, it is important to note that sound principles of trusting the environment of Artificial Intelligence (AI) systems are also required to characterize the trustworthiness of recommender systems. Otherwise, inadequate advice, high expectations and bad interpretations could lead to making bad choices or to demotivating job seekers. This paper expands on previous research, highlighting the point of view of trustworthiness in job recommender systems (JRS) and providing an overview of the dimensions of AI trustworthiness for the job domain. The purpose of this study is to investigate how trustworthy and suggestive outputs can improve the communication between a job mediator and a job seeker by enhancing the credibility of the information provided to job applicants and increasing customer satisfaction.