Mitigating Bias in Data Governance Models: Ethical Considerations for Enterprise Adoption

Abstract

In recent years, the growing reliance on data-driven decision-making in enterprises has highlighted the need for robust data governance models that ensure fairness, transparency, and accountability. However, the integration of artificial intelligence (AI) and machine learning (ML) technologies into business processes has introduced new challenges regarding the mitigation of biases within these models. This paper explores the ethical considerations surrounding the adoption of data governance frameworks in enterprises, emphasizing the critical role of addressing bias in data collection, processing, and decision-making. By reviewing current literature and case studies, the paper identifies key sources of bias—ranging from historical data inequalities to algorithmic biases—and presents strategies for mitigating these biases. These strategies include the implementation of fairness-aware algorithms, regular auditing practices, and fostering an inclusive data governance culture. The paper further discusses the implications of bias mitigation on corporate governance, stakeholder trust, and legal compliance. Ultimately, it underscores the importance of aligning data governance models with ethical standards to support equitable business practices and foster public confidence in enterprise data systems.