Convincing the Expert: Reducing Algorithm Aversion in Administrative Higher Education Decision-making
L. Xu, Zachary A. Pardos, A.L. Pai
Abstract
Algorithm aversion can be described as the tendency of human decision-makers to discount algorithmic recommendations more heavily than similar recommendations made by humans. It has been a phenomenon observed to be most acutely exhibited by domain experts. In our work, we focus on expert administrators in higher education making course credit equivalency decisions that affect the academic planning and potential degree progress of millions of prospective transfer students. Using human-centered design, we construct an AI-based platform for recommending matches to courses on a student's transcript to courses offered at another institution. We conduct a 2 x 2, between-subject experiment to investigate potential aversion mitigation techniques by manipulating the presence of outliers and allowing users to provide feedback to the algorithm. Our findings indicate that intentional, human-centered design and careful presentation of algorithm-based recommendations can help improve Human-AI interaction and productivity with implications for various domains of expertise.