Ensuring data privacy in AI/ML implementation

Abstract

AbstractArtificial intelligence (AI) and machine learning (ML) have transformed the landscape of data management in higher education institutions, necessitating a critical evaluation of existing data privacy policies and practices. This research delves into the inadequacies of current frameworks in adapting to the swift evolution of Big Data. Student, faculty, and staff perspectives on data privacy are examined in terms of how their viewpoints influence university policies. Data privacy incidents at several universities are discussed to identify patterns and extract lessons learned. Recommendations and best practices for enhancing data privacy in the context of AI/ML implementation are discussed, with an emphasis on the need for policy reform and improved protocols to safeguard student privacy effectively. The integration of AI/ML in higher education must align with robust data privacy standards, enabling institutions to optimize teaching, learning, and administrative processes while safeguarding sensitive student information.Practical Takeaways AI/ML advancements necessitate a critical review of data privacy policies in higher education to adapt to Big Data evolution. The perspectives of students, faculty, and staff are crucial in shaping effective data privacy policies. Analysis of data privacy incidents offers insights for policy reform and implementation of best practices. Recommendations emphasize the importance of policy reform and robust protocols to protect student privacy amidst AI/ML integration, ensuring effective teaching, learning, and administrative processes.