Data Governance, Bias Mitigation, And Legal Risk: A Holistic AI Compliance Framework for U.S. Companies in High Stakes Sectors

Abstract

This study examines data governance, bias mitigation, and legal risk within the context of a holistic Artificial Intelligence (AI) compliance framework for US companies operating in high-stakes sectors such as healthcare, finance, insurance, and critical infrastructure. The rapid adoption of AI systems has improved efficiency and decision-making capabilities. However, it has also introduced significant challenges related to algorithmic bias, lack of transparency, weak data governance, and increasing legal and regulatory exposure. Drawing on existing literature, the study highlights that inadequate governance structures and poor-quality datasets contribute to discriminatory outcomes, reduced accountability, and heightened compliance risks under evolving regulatory regimes. It further shows that algorithmic bias persists due to historical data inequalities and opaque machine-learning models, while legal frameworks such as privacy and anti-discrimination laws place additional obligations on organizations deploying AI systems. The study concludes that a holistic AI compliance framework integrating data governance, bias mitigation strategies, legal oversight, cybersecurity, and ethical accountability is essential for ensuring responsible and trustworthy AI deployment in high-stakes environments.