Insights into University Composite Rankings from Explainable AI Counterfactuals
Abstract
University Rankings exert considerable influence in higher-education decision-making. Yet, as an artifact of their construction, rankings are largely unhelpful in conveying practical strategic insights to university administrators intent on improving their college’s rank. Machine learning tools such as interpretable machine learning (IML) and explainable artificial intelligence (XAI), taking aim at piercing obscure, black-box algorithms have gained a lot of interest recently. However, there appear to be few deployments of their use in appraising University rankings. In this work, using data representing QS Rankings data of USA MBA programs we show how counterfactual XAI can support proactive responses by educational stakeholders to Rankings outcomes. Explaining individual predictions opens great opportunities for intervention and strategizing. The method is applicable to any extant rankings.
Copyright (c) 2024 Armando Rodriguez, George Heudorfer, Brian Kench
This work is licensed under a Creative Commons Attribution 4.0 International License.
Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.
Copyrights for articles published in MTI journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.
