In a world increasingly captivated by the opportunities and challenges of artificial intelligence (AI), there has been a surge in the establishment of committees, forums, and summits dedicated to AI governance. While crucial, these platforms often overlook a fundamental pillar: the role of data governance.
The current focus on AI governance, with its myriad ethical, legal, and societal implications, tends to sidestep the fact that effective AI governance is, at its core, reliant on the principles and practices of data governance. This oversight has resulted in a fragmented approach, leading to a scenario where the data and AI communities operate in isolation, often unaware of the essential synergy that should exist between them.
This essay delves into the intertwined nature of these two realms. It provides six reasons why AI governance is unattainable without a comprehensive and robust framework of data governance. In addressing this intersection, the essay aims to shed light on the necessity of integrating data governance more prominently into the conversation on AI, thereby fostering a more cohesive and effective approach to the governance of this transformative technology.
1. Data governance covers the full data lifecycle, of which Artificial Intelligence is a part.
2. Data governance enables the development of responsible, fit-for-purpose AI systems.
3. Data governance takes care of issues that AI systems would otherwise inherit.
4. Data governance is required to establish a social license for AI systems.
5. Data governance is technology-agnostic, and thus more holistic in nature.
6. The implementation, standardization and codification of data governance provide valuable lessons for AI governance.
Read the blog post: Interwoven Realms: Data Governance as the Bedrock for AI Governance
