Confronting Algorithmic Bias: Equity-Centered Leadership Responses to Generative AI in Higher Education

• In Event: Negotiating Power and Translating Technology Policies and Standards into Practice

Abstract

This case study examines how algorithmic bias in generative artificial intelligence (AI) tools affects faculty of color and challenges equity commitments in higher education. When Dr. Jacqueline Rivers, a Black woman faculty member, encountered racially biased outputs from an institution-endorsed AI system, her experience exposed a broader institutional dilemma: how to balance innovation, equity, and leadership responsibility. This paper uses an integrated theoretical framework to analyze the leadership, ethical, and systemic implications of AI deployment in education. It urges educational leaders to implement inclusive technology governance practices and centers the lived experiences of historically marginalized faculty in shaping institutional responses to emerging technologies.

Author

• Pinyi Wang, University of Florida