* Corresponding author: Neelam Gupta Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. How inadequate data governance frameworks lead to unethical outcomes in Artificial Intelligence Systems Neelam Gupta * Director, Data and Artificial Intelligence, Avanade, United States. International Journal of Science and Research Archive, 2022, 07(01), 580-590 Publication history: Received on 22 June 2022; revised on 24 October 2022; accepted on 27 October 2022 Article DOI: https://doi.org/10.30574/ijsra.2022.7.1.0274 Abstract The increasing adoption of artificial intelligence (AI) technology in decision-making has made incredible advances, but it also has significant ethical problems. A crucial, yet often ignored, factor is insufficient data governance practices. This article examines how inadequate data governance practices, such as a lack of accountability, weak privacy protections, a lack of quality control in data management, and weak traceability, contribute to unethical outcomes with AI. Using relevant case studies and promising practices for consideration, we conclude that data governance is at the core of the ethical use of AI. The paper ends with public policy recommendations and organizational approaches to attenuate risks and enhance fairness, transparency, and accountability in AI. Keywords: Data Governance; Artificial Intelligence Ethics; Data Quality; Algorithmic Accountability; Data Privacy; Responsible AI; Data Stewardship; Transparency in AI; AI Risk Management 1. Introduction Artificial intelligence (AI) systems are increasingly being used to automate complex decisions in many fields from health care to finance to criminal justice and education. AI systems rely on large amounts of data to perform as intended. The ethical potential of AI is contingent not just on the algorithms but also whether there is strong oversight of the data itself. Data governance refers to the people, processes, policies, standards, and technologies that enable the appropriate ethical and effective management of data throughout its lifecycle. Data governance is a crucial part of ensuring, specifically in the context of AI systems, that they are transparent, accountable, ethical and fair. We recognize that, ethical AI systems are possible, sustainable and viable even though many organizations are using AI without appropriate governance frameworks that result in opaque, biased and sometimes harmful decision outcomes. There has been significant discussion regarding the intersection of AI and ethics, however the relationship between the absence of strong data governance frameworks and unethical AI decision-making is under-researched. This could be framed as an identified gap in the research. We discuss the causal mechanisms connecting weak data governance to ethical breaches including bias, privacy violations, discrimination and other ethical issues. Drawing on case examples and our policy recommendations, we provide recommendations for the adoption of ethical governance within the development and deployment of AI.