AI TIPS 2.0: A Comprehensive Framework for Operationalizing AI Governance
This work addresses governance challenges for organizations deploying AI systems, but it appears incremental as an update to an existing framework.
The paper tackles the problem of inadequate AI governance frameworks by proposing AI TIPS 2.0, which addresses challenges in risk assessment, actionable controls, and operationalization at scale, resulting in a comprehensive framework for embedding trustworthy AI practices throughout the development lifecycle.
The deployment of AI systems faces three critical governance challenges that current frameworks fail to adequately address. First, organizations struggle with inadequate risk assessment at the use case level, exemplified by the Humana class action lawsuit and other high impact cases where an AI system deployed to production exhibited both significant bias and high error rates, resulting in improper healthcare claim denials. Each AI use case presents unique risk profiles requiring tailored governance, yet most frameworks provide one size fits all guidance. Second, existing frameworks like ISO 42001 and NIST AI RMF remain at high conceptual levels, offering principles without actionable controls, leaving practitioners unable to translate governance requirements into specific technical implementations. Third, organizations lack mechanisms for operationalizing governance at scale, with no systematic approach to embed trustworthy AI practices throughout the development lifecycle, measure compliance quantitatively, or provide role-appropriate visibility from boards to data scientists. We present AI TIPS, Artificial Intelligence Trust-Integrated Pillars for Sustainability 2.0, update to the comprehensive operational framework developed in 2019,four years before NIST's AI Risk Management Framework, that directly addresses these challenges.