Predictive Algorithms and Racial Bias: A Qualitative Descriptive Study on the Perceptions of Algorithm Accuracy in Higher Education

Autor/in	von Winckelmann, Stacey Lynn
Titel	Predictive Algorithms and Racial Bias: A Qualitative Descriptive Study on the Perceptions of Algorithm Accuracy in Higher Education
Quelle	In: Information and Learning Sciences, 124 (2023) 9-10, S.349-371 (23 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2398-5348
DOI	10.1108/ILS-05-2023-0045
Schlagwörter	Prediction; Algorithms; Racism; Accuracy; Higher Education; Social Justice; Student Records; Privacy + Suchen Sie Ihr Suchwort? Vorhersage; Algorithm; Algorithmus; Rassismus; Hochschulbildung; Hochschulsystem; Hochschulwesen; Soziale Gerechtigkeit; Schülerakte; Privatsphäre
Abstract	Purpose: This study aims to explore the perception of algorithm accuracy among data professionals in higher education. Design/methodology/approach: Social justice theory guided the qualitative descriptive study and emphasized four principles: access, participation, equity and human rights. Data collection included eight online open-ended questionnaires and six semi-structured interviews. Participants included higher education professionals who have worked with predictive algorithm (PA) recommendations programmed with student data. Findings: Participants are aware of systemic and racial bias in their PA inputs and outputs and acknowledge their responsibility to ethically use PA recommendations with students in historically underrepresented groups (HUGs). For some participants, examining these topics through the lens of social justice was a new experience, which caused them to look at PAs in new ways. Research limitations/implications: Small sample size is a limitation of the study. Implications for practice include increased stakeholder training, creating an ethical data strategy that protects students, incorporating adverse childhood experiences data with algorithm recommendations, and applying a modified critical race theory framework to algorithm outputs. Originality/value: The study explored the perception of algorithm accuracy among data professionals in higher education. Examining this topic through a social justice lens contributes to limited research in the field. It also presents implications for addressing racial bias when using PAs with students in HUGs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01