Insights into University Composite Rankings from Explainable AI Counterfactuals
Year: 2024
Type: article
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.
Authors: Armando Rodriguez, George Heudorfer, Brian Kench
Institution: University of New Haven
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404660400