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Abstract

Higher education institutions increasingly rely on artificial intelligence (AI)-enabled tools and predictive analytics to inform decisions in areas such as admissions, student success, and resource allocation. This reliance heightens the need for data governance frameworks that address the policies, processes, and technologies governing student data. Data governors shape how institutional data are collected, interpreted, and used, yet existing approaches often assume that data are neutral, overlooking how bias can become embedded and compounded across the stages of the data lifecycle. This exploratory study investigates whether data governors perceive implicit bias as present across these stages. Using a quantitative survey, we gathered responses from 36 executive-level data governors at R1 public land-grant institutions. Findings indicate a consensus that implicit bias can exist across three phases of the data lifecycle. A one-sample ttest indicates that perceptions of bias significantly exceed the scale midpoint, with substantial effect sizes reflecting consistent agreement. Viewed through the lens of algorithmic accountability, which highlights transparency, responsibility, and fairness, the results suggest that governance should include auditable, explainable, and contestable practices to investigate and mitigate bias. These insights are especially salient as institutions explore AI tools within their datainformed decision models.

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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

This work is archived and distributed under the repository's Standard Copyright and Reuse License (opens in new tab). End users may copy, store, and distribute this work without restriction. For all other uses, permission must be obtained from the copyright owners or their authorized agents.

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