Published: 17 August 2022
Enterprises need AI-specific governance to reduce risks and tolerate the complexity intrinsic to AI. Although there are many essential activities on the path to AI governance success, data and analytics leaders should take four practical actions to make immediate business impact.
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Enterprise leaders are taking steps to advance AI governance beyond just principles. They must operationalize AI governance to enable AI-powered systems to follow principles, such as trust, transparency, human-centricity, compliance, data security and privacy.
Because AI governance is new to the enterprise, it’s often separated from existing governance practices. However, AI governance is more successful if it extends existing governance practices with AI-specific considerations.
AI is inherently hard to govern because enterprises must meet the demands for safety and value under conditions that involve complexity. This complexity and the AI life cycle’s relative novelty lead to a lack of
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