Source: JDSupra

This article emphasizes that the foundation of any responsible artificial intelligence program is a robust and ethical approach to data management. The authors argue that while conversations about responsible AI often focus on algorithms and outcomes, the quality, provenance, and governance of the underlying data are the most critical factors in determining whether an AI system will operate fairly, accurately, and in compliance with the law. Without a strong data foundation, attempts to build responsible AI are destined to fail.

The analysis outlines several key data-related considerations for organizations adopting AI. These include ensuring data accuracy and completeness, actively working to identify and mitigate biases within datasets, and maintaining transparency about data sources and processing methods. Furthermore, the article stresses the importance of data privacy and security, adhering to principles like data minimization and purpose limitation to respect individual rights. A comprehensive data governance strategy is presented not just as a compliance requirement, but as an essential prerequisite for building trust with customers and regulators in the AI-powered economy.