Abstract

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption in business and research due to their ability to automate processes and provide predictive insights that exceed human capabilities. While businesses use AI/ML models to improve customer personalization, detect fraud, enhance strategic forecasting, and optimize supply chains, researchers analyze large, complex datasets and advance discoveries in fields like genomics and economics. Amidst these benefits, there is a growing concern about the risks associated with autonomous systems operating without adequate oversight (Eskandarany, 2024). For example, the disruption caused at PocketOS when an AI agent deleted its organizational data (Mansoor, 2026). The opacity of complex algorithms makes it difficult for organizations to justify or audit their analytics-driven actions. Hence, we argue for formal analytics governance to improve public trust and minimize legal and regulatory scrutiny for organizations and thus propose an AI/ML analytics governance framework. Table 1 summarizes our proposed framework.

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