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Michael Hind

Distinguished Research Staff Member, IBM

Michael Hind is a Distinguished Research Staff Member in IBM Research's AI department, who has worked on AI governance for much of the past decade and is now also pursuing ideas in generative computing, according to the IBM Research profile. The profile credits Hind and collaborators with transferring FactSheets and AI risk tools into IBM and with launching open-source projects including AI Fairness 360, AI Explainability 360, and AI Risk Atlas Nexus, alongside programming-language projects such as Jikes RVM and WALA. Hind is a co-author of the papers introducing AI Fairness 360, AI Explainability 360, FactSheets, and IBM's Granite Guardian models, and the profile notes the SIGPLAN Software Award in 2012 for work on Jikes RVM.12345

At a glance

Current role
Distinguished Research Staff Member
Company
IBM
Sector
Company
Education
NYU
Work listed
3
Honors
3
Sources cited
9
Profile reviewed

Career

Michael Hind received a PhD from NYU in 1991 and spent seven years as an assistant and then associate professor of computer science at SUNY New Paltz. At IBM Research, Hind has led researchers working on programming languages, software engineering, cloud computing and tools for AI systems.1

Hind's earlier IBM work includes the Jikes RVM research virtual machine and the WALA program analysis libraries, and Hind's profile also lists X10 and OpenWhisk among the open-source projects launched with collaborators. A 2000 paper by Hind on adaptive optimization was recognized as the OOPSLA 2000 Most Influential Paper.1

AI governance and trustworthy AI

For much of the past decade Hind has worked on AI governance. Hind co-authored the 2018 FactSheets paper, which proposes supplier's declarations of conformity documenting how AI services were built and tested, and IBM's profile says FactSheets, AI Risk Atlas and related risk tools were transferred to various parts of IBM.14

Hind is a co-author of the papers introducing the AI Fairness 360 toolkit (2018) for detecting and mitigating bias, and the AI Explainability 360 toolkit and taxonomy (2019). More recent work includes the Granite Guardian models for detecting risks in prompts and responses (2024) and the open-source AI Risk Atlas Nexus project.26357

Work and contributions

  • Granite Guardian Open weights

    Listed among the authors of the Granite Guardian technical report (2024), which introduces risk-detection models for LLM prompts and responses released as open source; the repository is under the Apache-2.0 license and the Granite Guardian 3.0 8B model card lists the apache-2.0 license.589

  • AI Fairness 360 and AI Explainability 360 Open-source code

    Co-author of the 2018 paper introducing AI Fairness 360, released under the Apache v2.0 license, and of the 2019 paper introducing the open-source AI Explainability 360 toolkit; IBM Research's profile lists both among the open-source projects Hind and collaborators launched.2631

  • FactSheets and AI Risk Atlas Nexus Open-source code

    Co-author of the 2018 FactSheets paper on documenting AI services; the IBM Research profile lists AI FactSheets 360 and AI Risk Atlas Nexus among Hind's projects, and the IBM/ai-atlas-nexus repository is under the Apache-2.0 license.417

Timeline

  1. Received a PhD from NYU before seven years on the computer science faculty at SUNY New Paltz.1
  2. Published a paper on adaptive optimization later recognized as the OOPSLA 2000 Most Influential Paper.1
  3. Recognized with collaborators by the SIGPLAN Software Award for Jikes RVM.1
  4. Co-authored the FactSheets paper on supplier's declarations of conformity for AI services.4
  5. Co-authored the paper introducing the AI Fairness 360 toolkit.2
  6. Co-authored the paper introducing the AI Explainability 360 toolkit.3
  7. Co-authored the Granite Guardian paper on risk-detection models.589

Honors and recognition

  • ACM Distinguished Scientist · Association for Computing Machinery1
  • OOPSLA Most Influential Paper (OOPSLA 2000 paper on adaptive optimization) · ACM SIGPLAN (OOPSLA)1
  • SIGPLAN Software Award (Jikes RVM) · ACM SIGPLAN, 20121

Where they work

  • IBM 29 people in the wiki

Suggest a correction or removal

Profiles cover public professional work only, and each statement cites a source. If something is out of date, missing or wrong, tell us and link a source. People can also ask for their profile to be corrected or removed.

References

9 sources · profile reviewed Oct 21, 2026 · data updated Oct 21, 2026

  1. Michael Hind — IBM ResearchIBM Research · Official page · accessed Oct 21, 2026
  2. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias (arXiv 1810.01943)arXiv · Paper · published Oct 3, 2018 · accessed Oct 21, 2026
  3. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques (arXiv 1909.03012)arXiv · Paper · published Sep 6, 2019 · accessed Oct 21, 2026
  4. FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity (arXiv 1808.07261)arXiv · Paper · published Aug 22, 2018 · accessed Oct 21, 2026
  5. Granite Guardian (arXiv 2412.07724)arXiv · Paper · published Dec 10, 2024 · accessed Oct 21, 2026
  6. Trusted-AI/AIF360 README and license (Apache-2.0)Trusted-AI (GitHub repository) · Repository · accessed Oct 21, 2026
  7. IBM/ai-atlas-nexus README and license (Apache-2.0)IBM (GitHub repository) · Repository · accessed Oct 21, 2026
  8. ibm-granite/granite-guardian README and license (Apache-2.0)IBM Granite (GitHub repository) · Repository · accessed Oct 21, 2026
  9. ibm-granite/granite-guardian-3.0-8b model card (license apache-2.0)IBM Granite (Hugging Face) · Model card · published Oct 15, 2024 · accessed Oct 21, 2026