AI Governance Deep Dive with Michael Hind, Distinguished Research Staff Member at IBM

Jan 27, 2026

Michael Hind, Distinguished Research Staff Member at IBM who leads work on AI governance and the AI Risk Atlas. He explains enterprise vs societal governance. Covers IBM’s Risk Atlas and taxonomies. Talks model risk scoring, runtime guardrails, limits of testing, transparency vs explainability, regulation design, insurance approaches, and tools like Granite Guardian and Benchmark Cards.

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INSIGHT

Two Distinct Faces Of AI Governance

AI governance has two lenses: enterprise risk management and societal impact requiring different priorities.
Michael Hind contrasts protecting a company's operations with broader societal questions like regulation and public trust.

ADVICE

Start By Mapping Risks To Your Use Case

Identify relevant risks for a specific use case before testing or deploying AI systems.
IBM's AI Risk Atlas and a TurboTax-like questionnaire map ~70 risks to concrete examples to focus evaluation efforts.

ADVICE

Use Automated Evals And Percentiles To Compare Models

Test models against the identified risks using automated evaluations and score results to compare models.
IBM's model risk evaluation runs targeted datasets and returns a 0–1 score and percentile versus other measured models.

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In the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.

In this episode, Alec welcomes back Michael Hind, Distinguished Research Staff Member at IBM. This episode is a special deep dive focused exclusively on the evolving field of AI governance. Michael defines AI governance from both enterprise and societal perspectives, highlighting the challenges of managing risk in rapidly evolving AI systems. He shares insights from his recent research, including the development of the AI Risk Atlas and model risk evaluation tools, and discusses the complexities of testing AI models and the importance of accurate benchmarking. The conversation covers the state of regulation, the intersection of insurance and AI risk, the role of transparency and explainability, and emerging technical solutions like entity tagging in LLMs. Alec and Michael conclude by emphasizing the need for industry-driven governance and enhanced transparency through tools such as Granite Guardian and Benchmark Cards.

Summary:

Defining AI Governance: Michael Hind explains the dual perspectives of AI governance—enterprise risk management and societal impact—and discusses the need for clear taxonomies.
Taxonomies and Risk Evaluation: IBM’s AI Risk Atlas and model risk evaluation tools help organizations identify, test, and monitor relevant AI risks for specific use cases.
Regulation and Industry Responsibility: With global regulation slowing, Michael argues for proactive enterprise governance, transparency, and industry benchmarks to fill the gap.
Testing, Explainability, and Transparency: The episode explores the limits of model evaluation, the challenge of explainability, and the need for public transparency, including the Stanford Transparency Index.
Insurance and Technical Advances: The dialogue addresses how insurance may eventually adapt to AI risk, and highlights new approaches like entity tagging and fault-tolerant generative computing.

Companies/Organizations:

IBM
Artificial Intelligence Risk, Inc.
NIST
MIT
Stanford University
Notre Dame
Drainpipe IO
OpenAI
Google