Dokument: How is Socially Responsible Academic Performance Prediction Possible? Insights from a Concept of Perceived AI Fairness.

Titel:	How is Socially Responsible Academic Performance Prediction Possible? Insights from a Concept of Perceived AI Fairness.
URL für Lesezeichen:	https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=62553
URN (NBN):	urn:nbn:de:hbz:061-20230512-123737-8
Kollektion:	Publikationen
Sprache:	Englisch
Dokumententyp:	Wissenschaftliche Texte » Artikel, Aufsatz
Medientyp:	Text
Autoren:	Keller, Birte [Autor] Dr. Lünich, Marco [Autor] Prof. Dr. Marcinkowski, Frank [Autor]
Dateien:	[Dateien anzeigen] Adobe PDF [Details] 620,6 KB in einer Datei [ZIP-Datei erzeugen] Dateien vom 12.05.2023 / geändert 12.05.2023
Stichwörter:	Organizational Justice, Fairness, Academic Performance Prediction, Dropout, Study Success, Students, Perceptions, Accountability, Input Data, Explainability, Interventions, Algorithmic Design
Beschreibung:	The availability of big data at universities enables the use of artificial intelligence (AI) systems in almost all areas of the institution: from administration to research, to learning and teaching, the use of AI systems is seen as having great potential. One promising area is academic performance prediction (APP), which is expected to provide individual feedback for students, improve their academic performance, and ultimately increase graduation rates. However, using an APP system also entails certain risks of discrimination against individual groups of students. Thus, the fairness perceptions of affected students come into focus. To take a closer look at these perceptions, this chapter develops a framework of the "perceived fairness" of an ideal-typical APP system, which asks critical questions about input, throughput, and output and, based on the four-dimensional concept of organizational justice (Greenberg, 1993), sheds light on potential (un-) fairness perceptions from the students' point of view.
Rechtliche Vermerke:	Originalveröffentlichung: Keller, Birte; Lünich, Marco; Marcinkowski, Frank (2022): How Is Socially Responsible Academic Performance Prediction Possible? Insights From a Concept of Perceived AI Fairness. In Fernando Almaraz-Menéndez, Alexander Maz-Machado, Carmen López-Esteban, Cristina Almaraz-López (Eds.): Strategy, Policy, Practice, and Governance for AI in Higher Education Institutions: IGI Global (Advances in Higher Education and Professional Development), pp. 126-155. DOI: 10.4018/978-1-7998-9247-2.ch006.
Lizenz:	Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz
Fachbereich / Einrichtung:	Philosophische Fakultät » Sozialwissenschaftliches Institut
Dokument erstellt am:	12.05.2023
Dateien geändert am:	12.05.2023