AI TIPS 2.0: A Comprehensive Framework for Operationalizing AI Governance
Pamela Gupta

TL;DR
AI TIPS 2.0 introduces a detailed operational framework to improve AI governance by addressing risk assessment, actionable controls, and scalable trust practices, filling gaps left by existing standards.
Contribution
It provides a comprehensive, practical framework for operationalizing AI governance tailored to diverse use cases, with mechanisms for compliance measurement and lifecycle integration.
Findings
Addresses specific governance challenges with tailored solutions.
Provides actionable controls aligned with high-level principles.
Enables scalable, lifecycle-based AI trust management.
Abstract
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…
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Taxonomy
TopicsEthics and Social Impacts of AI · Artificial Intelligence in Healthcare and Education · Explainable Artificial Intelligence (XAI)
