Paper on the potential discrimination in the student advisory process

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At the AMCIS 2021 Daniel Schoemer, Sven Laumer, Karl Wilbers, Tobias Wolbring, Jonas Weigert and Dr. Edgar Treischl today presented their paper “Data-driven Student Advisory and Potential Direct Discrimination: A Literature Review on Machine Learning for Predicting Students’ Academic Success”. This paper was awarded to be among the 25 best papers, which is a great success for the authors.

In their study, they systematically reviewed and identified scholarly papers on Artificial Intelligence that use sensitive attributes to predict students’ academic success, which are not allowed to be considered in the decision-making process due to legal restrictions. The goal was to find out what attributes have been used, that potentially might lead to direct discrimination in data-driven student advisory.

The review shows that out of 95 studies, over 52 percent use at least one discriminating attribute. This clearly highlights the need to explore causal mechanisms to prevent potential discrimination or seek alternative solutions without using protected individual characteristics.