Michael Hind

Title

Distinguished Research Staff Member

Bio

Michael Hind is a Distinguished Research Staff Member in the IBM Research AI department in Yorktown Heights, New York. His current research passion is in the general of area of Trusted AI, focusing on the fairness, explainability, transparency, and the goverance of AI systems. He currently leads the FactSheets project at IBM Research.

Michael has led dozens of researchers focusing on programming languages, software engineering, cloud computing, and tools for AI systems. Michael's team has successfully transferred technology to various parts of IBM (FactSheets, AI Risk Atlas, AI Risk Identification, and Model Risk Evaluation), and launched several successful open source projects, Jikes RVM, X10, WALA, OpenWhisk, and more recently AI Fairness 360, AI Explainability 360, and AI Risk Nexus. After receiving his Ph.D. from NYU in 1991, Michael spent 7 years as an assistant/associate professor of computer science at SUNY - New Paltz.

Michael is an ACM Distinguished Scientist, and was elected to IBM's Academy of Technology. He has co-authored over 50 publications, served on over 50 program committees, and given many keynotes and invited talks at top universities, conferences, and government settings. His 2000 paper on Adaptive Optimization was recognized as the OOPSLA'00 Most Influential Paper and his work on Jikes RVM was recognized with the SIGPLAN Software Award in 2012.

Check out these open source and research projects

Invited Talks/Panels/Interviews

Convening to Act: Accelerating National Security AI Adoption panel on Future of AI Acquisition, March 27-28, 2025
Transparency in Discussion: Improving Transparency and Accountability in AI Implementations podcast panel by Humanitarian AI Today, November 23, 2024
Operationalising AI Governance, with Francesca Rossi and Paul Dongha (NatWest) at NatWest's Data Science annual conference (DSEC), Edinburgh, Scotland, November 19, 2024
3rd Workshop on Synthetic Data and GenAI in Finance workshop (panelist), ICAIF'24 (ACM International Conference on AI in Finance), Brooklyn, NY, November 14, 2024
VIVEKFEST, Vivek Sarkar Festschrift Symposium, SPLASH 2024 workshop, October 21, 2024
AI: Legal Ethics and Regulatory Measures (panel), Seton Hall Law School 's Journal of Legislation and Public Policy Symposium, October 18, 2024
Cornell University, Public Interest Technology Class, AI Trends, Challenges, and the Role of Public Policy, guest lecture, July 25, 2024
All Things AI Podcast interview, July 9, 2024
AI Risk Reward podcast interview on Trustworthy AI, March 19, 2024
AI Safety and Robustness in Finance workshop (invited talk & panel), ICAIF'23 (ACM International Conference on AI in Finance), Brooklyn, NY, November 27, 2023
Nassau Country TRACT Teacher Center's Technology Conference, November 15, 2023
Trustworthy AI: Challenges and Opportunities (keynote), 2nd SNAS Annual Academic Conference, Fairleigh Dickinson University, Madison, NJ, Oct 6, 2023
Presidential Symposium, Navigating the AI Revolution: Definitions and the Impact on Workforce Across Industries, (panel), Hofstra University, September 28, 2023 (video)
BSA Congressional Briefing: Everyday AI: Managing AI Risk Today (panel), September 26, 2023
Cornell University, Public Interest Technology Class, AI Trends, Challenges, and the Role of Public Policy, guest lecture, August 1, 2023
BSA Congressional Briefing: AI in Financial Services (panel), June 21, 2023
FactSheets: Increasing AI Transparency, Enabling Governance, and Assessing Risk (invited talk), SEC Quant Seminar, June 8, 2023
AI in the Built World: Who Should Be The Adults in The Room? (panel), Cherre Data Summit, May 17, 2023
FactSheets: Increasing AI Transparency, Enabling Governance, and Assessing Risk (keynote), Trustworthy AI Summit, Jan 11, 2023
Trustworthy AI, Nokia Bell Labs, Oct 6, 2022
Practical Approaches to Effectively Manage Transparency (invited talk), Epstein Becker Green's Explainable Artificial Intelligence and Transparency Virtual Briefing, Jun 9, 2022
Defining AI Impact Assessments; Industry Perspectives (panel), Bipartisan Policy Center, May 18, 2022 [video]
Business at OECD (BIAC) Webinar on Trustworthy AI (panelist), Apr 21, 2022
Lessons from IBM on how to create Trustworthy AI (keynote talk), Sony Technical Exchange Fair, Dec 2, 2021
Artificial Intelligence and You podcast interview on AI Explainability, Part 1 (Nov 15, 2021), Part 2 (Nov 22, 2021)
Open Science & Good Research Practice (panel), Third symposium on Biases in Human Computation and Crowdsourcing, November 10, 2021
Measuring with Purpose (panel), NIST AI Measurement and Evaluation Workshop, June 15-17, 2021
AI Governance: Driving Compliance, Efficiency, and Outcomes, Enterprise Data World, April 21, 2021
Trusted AI, University of British Columbia, part of Trustworthy Machine Learning course, April 7, 2021
AI Governance: Driving Compliance, Efficiency, and Outcomes with RBC Bank (keynote), Chief Data & Analytics Officers Financial Services Conference, March 3, 2021 (video)
Race, Tech, and Civil Society: Tools for Combating Bias in Datasets and Models (panel), Stanford Center for Comparative Studies in Race and Ethnicity, February 3, 2021 (video)
What are the roles of explanations throughout the AI life cycle? (panel), NIST Explainabile AI Workshop, January 26-28, 2021.
Increasing Trust and Transparency in AI, Singapore FinTech Festival, December 11, 2020
Trusted AI (invited talk), AI Journey Conference, December 3, 2020
Governance is key to embed AI at scale (panel), Digital Transformation World Series 2020, October 21, 2020
What Are the Research Challenges in Trusted AI? (invited talk), AI and Cybersecurity Issues in Financial Services, September 25, 2020
AI Explainability and Factsheets (interview) in series Trusting AI: Unlocking the Black Box (Episode 3), September 21, 2020
Establishing Trust with AI Ethics and Governance (panel), IBM Data & AI Virtual Forum, July 9, 2020
Increasing Trust in AI (guest lecture), Ethical Implications of AI, Goethe Universtat Frankfurt, June 17, 2020
Trusted AI (talk and panel), IEEE Albany Nanotechnology Symposium, November 12, 2019
Technology and Ethics: Opportunities and Challenges (panel), Law, Justice and Development Week, Washington DC, November 7, 2019
Bringing Trusted AI Research to Society (panel), IBM IT Legal Summit, New York City, October 22, 2019
AI Data and Infrastructure Workshop, National Security Commission on AI, Washington DC, September 18, 2019
Data, Inference & Algorithmic Fairness (talk and panel) at Tech Foundations for Congressional Staff Workshop, Georgetown Law, August 13, 2019
Increasing Trust in AI, (Panel) at Social Justice and Emerging Technologies Conference, CUNY Law School, April 13, 2019
TED: Teaching Explanations for Decisions, Alan Turing Institute, July 27, 2018
Expert Voices Live: An Ethical Approach to Innovation, Axios Roundtable, July 18, 2018
Why isn't the PL/SE Community Working on Cloud Computing?, Institue for Software Research, CMU, October 27, 2015
Changing the Foundation: How the Multicore Era has Impacted Software and What the Future Holds, Invited Course, ACACES'14 summer school, July 13-19, 2014
Changing the Foundation: The Impact of Multicore Architectures on Software SUNY New Paltz School of Science and Engineering Colloquium Series February 14, 2013.
Teaching Programming Language Design and Implementation ... What? To Whom? How?, PLDI'11 Panel, June 8, 2011.
The Impact of Multicore Architectures on Software: Disaster or Opportunity?, Invited Talk, University of Washington, October 20, 2009 (video). Also presented at Tokyo Tech Workshop (keynote), Ghent University, ICT (Beijing), and University of Illinois at Urbana-Champaign (2012).
What Role Does Code Generation and Optimization Play for Multi-Core Enablement? CGO'08 Panel, April 8, 2008.
Dynamic Compilation and Adaptive Optimization in Virtual Machines, Invited Course, ACACES'06 summer school, July 23-29, 2006
Why Software Optimization Matters and Some Thoughts on How to Improve It, Invited Talk, University of Illinois at Urbana-Champaign, April 27, 2005, (Also presented at University of Colorado and Seoul National University)
Virtual Machine Learning: Thinking like a Computer Architect, Keynote, CGO'05, March 21, 2005
The Jikes RVM Story, Invited Talk, Red Hat Free Java Summit, MIT, November 18-19, 2004
Using Jikes RVM to Understand the Hardware Performance of Java Applications, Keynote, MRE'03, March 23, 2003
Pointer Analysis: Haven't We Solved This Problem Yet? Invited Talk, PASTE'01, June 18-19, 2001

Publications

Developing a Risk Identification Framework for Foundation Model Uses, David Piorkowski, Michael Hind, John Richards, Jacquelyn Martino, June 2024
Quantitative AI Risk Assessments: Opportunities and Challenges, David Piorkowski, Michael Hind, John Richards, Seton Hall Journal of Legislation and Public Policy, Volume 49, Issue 3, 2025, 644-669.
AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources, Frank Bagehorn, Kristina Brimijoin, Elizabeth M. Daly, Jessica He, Michael Hind, Luis Garces-Erice, Christopher Giblin, Ioana Giurgiu, Jacquelyn Martino, Rahul Nair, David Piorkowski, Ambrish Rawat, John Richards, Sean Rooney, Dhaval Salwala, Seshu Tirupathi, Peter Urbanetz, Kush R. Varshney, Inge Vejsbjerg, Mira L. Wolf-Bauwens, Mar, 2025
Granite Guardian, Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia, Subhajit Chaudhury, Tejaswini Pedapati, Pierre Dognin, Keerthiram Murugesan, Erik Miehling, Martín Santillán Cooper, Kieran Fraser, Giulio Zizzo, Muhammad Zaid Hameed, Mark Purcell, Michael Desmond, Qian Pan, Inge Vejsbjerg, Elizabeth M. Daly, Michael Hind, Werner Geyer, Ambrish Rawat, Kush R. Varshney, Prasanna Sattigeri, NAACL 2025 Industry Track, April 2025
Usage Governance Advisor: from Intent to AI Governance, Elizabeth M. Daly, Sean Rooney, Seshu Tirupathi, Luis Garces-Erice, Inge Vejsbjerg, Frank Bagehorn, Dhaval Salwala, Christopher Giblin, Mira L. Wolf-Bauwens, Ioana Giurgiu, Michael Hind, Peter Urbanetz, December 2024, The 2nd International Workshop on AI Governance (AIGOV), part of AAAI'25, March 3, 2025
Agentic AI Needs a Systems Theory, Erik Miehling, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Matthew Riemer, Djallel Bouneffouf, John T. Richards, Amit Dhurandhar, Elizabeth M. Daly, Michael Hind, Prasanna Sattigeri, Dennis Wei, Ambrish Rawat, Jasmina Gajcin, Werner Geyer, Feb, 2025
Quantitative AI Risk Assessments: Opportunities and Challenges, David Piorkowski, Michael Hind, John Richards, (Revised) December 2024
BenchmarkCards: Large Language Model and Risk Reporting, Anna Sokol, Nuno Moniz, Elizabeth Daly, Michael Hind, Nitesh Chawla, October 2024
The CLeAR Documentation Framework for AI Transparency: Recommendations for Practitioners & Context for Policymakers, Harvard Kennedy School, Shorenstein Center on Media, Politics, and Public Policy, May 2024
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations, Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspooon, Marcel Zalmanovici, March, 2024
Assessing and implementing trustworthy AI across multiple dimensions, Chapter 12 in book Ethics in Online AI-based Systems: Risks and Opportunities in Current Technological Trends, Abigail Goldsteen, Ariel Farkash, Michael Hind, Elsevier, 2024
Quantitative AI Risk Assessments: Opportunities and Challenges, David Piorkowski, Michael Hind, John Richards, 2022
Evaluating a Methodology for Increasing AI Transparency: A Case Study, David Piokowski, John Richards, Michael Hind, 2022
A Human-Centered Methodology for Creating AI FactSheets, John Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilovic, and Kush R. Varshney, Bullletin of the Technical Committee on Data Engineering, December, pp. 47-58, 2021
AI Explainability 360: Impact and Design, Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang, 2021
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users, Tim Draws, Zoltan Szlavik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney and Michael Hind, PERSUASIVE 2021
Best Practices for Insuring AI Algorithms, Phaedra Boinodiris and Michael Hind, Cognitive World, 2020
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models Vijay Arya, Rachel Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang, Journal of Machine Learning Research (JMLR), Vol 21, 2020
Experiences with Improving the Transparency of AI Models and Services Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John Richards, Kush R. Varshney, CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness Michael Hind, Dennis Wei, Yunfeng Zhang, 2020
AI Fairness: How to Measure and Reduce Unwanted Bias in Machine Learning book, Trisha Mahoney, Kush Varshney, and Michael Hind, O'Reilly Media, 2020

MORE TO COME SOON Check Google Scholar

Awards, Services, and Other Activities

IBM Outstanding Technical Achievement Award, Factsheets, 2024
IBM Outstanding Technical Achievement Award, Factsheets, 2023
IBM Corporate Award, Trustworthy AI, 2021
IBM Outstanding Technical Achievement Award, Trustworthy AI, 2019
Co-Program Chair, OOPSLA'16 Artifact Evaluation Committee
Co-Program Chair, OOPSLA'15 Artifact Evaluation Committee
Member, ACM Publications Board Committee on Conferences, Jan 2014 - June 2014
Member at Large, SIGPLAN Executive Committee, July 2012 - June 2015
Chair, SIGPLAN Research Highlights Committee, July 2012 - June 2015
Chair, SIGPLAN Software Award Committee, July 2012 - June 2014
Member, NYU-Poly Enterprise Learning Board of Directors, 2012 - 2013
SIGPLAN Programming Languages Software Award, 2012 for Jikes RVM
General Chair, X10 Workshop at PLDI'11
Most Influential OOPSLA'00 Paper Award
PLDI Representative to SIGPLAN nominating committee for CACM Research Highlights, 2010 - 2012
Guest co-Editor, IBM Journal of R&D, special issue on Multicore Software, 54(5), 2010
ACM Distinguished Scientist, 2009
General Chair, PLDI'09
Tutorial Chair, PLDI'08
Associate Editor, ACM TACO, Jan 2006 - 2012?
Steering committee, PLDI Conference, June 2008 - May 2012
Steering committee, VEE Conference, June 2005 - April 2009, (chair June 2005 - August 2007)
General chair, VEE'05
General chair, Future of Virtual Execution Environments workshop, Sept 15-17, 2004. Full video available.
Steering committee, MASPLAS (Mid-Atlantic Student Workshop on Programming Languages and Systems)
General chair, MASPLAS'96, MASPLAS'01
Program chair, MRE'03
Advisory committee member, Computer Science Department, SUNY at New Paltz

Program Committees

2025: FAccT 2025 (Area Chair), GenAICHI 2025, AIES 2025
2024: FAccT 2024, Big Data 2024, GenAICHI 2024
2023: FAccT 2023, Big Data 2023
2022: AIES 2022, CHI'22 LBW (reviewer)
2021: AIES 2021
2020: FAT*2020, AIES 2020
2019: FAT*2019, AAAI 2019 (reviewer), HILL2019, HCML-19
2018: NIPS'18 (reviewer), WHI'18 (reviewer)
2017: CASCON'17
2016: PLDI'16 EPC, ISMM'16 ERC, ICPP'16
2015: LCPC'15, PLDI'15 ERC
2014: MUSEPAT'14, CASCON'14
2013: SAC'13 (PL Track)_, _ICPE'13, MUSEPAT'13, CASCON'13
2012 CASCON'12, SAC'12 (PL Track)
2011: X10 Workshop at PLDI, SAC'11 (PL Track)
2010: ASPLOS'10, PLDI'10 ERC, IWMSE'10, CASCON'10, SAC'10 (PL Track)
2009: HiPEAC'09, PACT'09, IISWC'09, CASCON'09,
2008: IISWC''08, CASCON'08, First Workshop on Programming Language Curricula
2007: WDDD 2007
2006: ASPLOS'06, PACT'06, CGO 2006, STMCS
2005: VEE'05, PLDI 2005, CGO 2005, MRE 2005
2004: ISSTA 2004, CC 2004, MRE 2004
2003: OOPSLA'03, Workshop on Exploring the Trace Space for Dynamic Optimization Techniques
2002: 4th Workshop on Binary Translation, JVM'02, ISSTA 2002, ECOOP'02 Workshop on Resource Management for Safe Languages
2001: FDDO'01

Tutorials and Courses

AI Fairness 360
- ACM FAT*2019 Conference, January 29, 2019, Video, Slides
Changing the Foundation: How the Multicore Era has Impacted Software and What the Future Holds
- ACACES'14 4-day course, July 13-19, 2014
Dynamic Compilation and Adaptive Optimization in Virtual Machines
- ACACES'06 4-day course, July 23-29, 2006, Slides
- ETAPS 2005, April 3, 2005.
- NEPLS'04,October 8, 2004, See PLDI'04 Slides
- PLDI'04, June 8, 2004, Slides
- CGO'04, March 21, 2004, See PLDI'04 Slides
The Design and Implementation of the Jikes RVM Optimizing Compiler
- OOPSLA'02, November 4, 2002, (expanded version of PLDI'02 tutorial), Slides
- PLDI'02, June 16, 2002, Slides
The Design and Implementation of the Jalapeno Research VM for Java PACT'01, September 9, 2001, Slides