Streamline the AI lifecycle, ensure traceability, and systematize compliance

AI governance involves the application of rules, processes and responsibilities to maximize the value of automated data products while ensuring ethical practices and mitigating risks. AI governance is crucial for organizations leveraging any AI use case, including generative AI. AI is a business-critical capability, with AI governance an essential organizational need to drive maximum value and ROI while maintaining ethical standards and compliance.

One of the biggest challenges for business and data leaders pursuing AI initiatives is the choice between a data-centric or model-centric approach. At Collibra, we know data is at the heart of any data product, and nowhere is this more true than with AI models. Today, the AI community is moving toward a consensus that data quality and consistency improve AI accuracy more efficiently for most businesses than tweaking models.

A reliable AI governance framework, such as Collibra's continuous 4-step process, can improve compliance and reduce risk. Such a framework includes clear use case definition, data quality assessments, model creation and analysis and ongoing verification and monitoring for compliance. Leveraging an AI governance framework optimizes AI development, minimizes risks and ensures ethical and responsible use of AI — most importantly, it provides a reliable, repeatable approach for AI initiatives.

AI governance assists data scientists by providing transparency in AI models, eliminating the "black box" aspect. It enables easy cataloging and tracking of model performance, aiding in efficient management and issue resolution. Moreover, governance helps ensure compliance with regulations, protecting data scientists and organizations. Future advancements promise increased efficiency and improved communication for data scientists. Most importantly, AI governance can help ensure data scientists have access to high-quality, trusted data.

AI governance is the application of rules, processes, and responsibilities to drive maximum value from your automated data products by ensuring applicable, streamlined, and ethical AI practices that mitigate risk, and protect privacy. Because AI models need high-quality data to provide the best possible output, AI governance falls under the larger, enterprise data governance umbrella. Both AI governance and data governance set the appropriate standards and policies around data used within an organization to reduce risk, ensure compliance, and increase productivity.

Our artificial intelligence (AI) governance glossary provides concise explanations for key AI governance terms, expertly curated for professionals in data, IT, risk and legal sectors by Collibra’s data experts.