Michael Hind

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 and launched several successful open source projects, Jikes RVM, X10, WALA, OpenWhisk, and more recenty AI Fairness 360 and AI Explainability 360. 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 a member of 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

• AI Fairness 360
• AI Explainability 360
• AI FactSheets 360

Invited Talks/Panels/Interviews

• 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

• 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, 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

Google Scholar

• Quantitative AI Risk Assessments: Opportunities and Challenges, Michael Hind, David Piorkowski, 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

• MORE TO COME SOON

Awards, Services, and Other Activities

• IBM Outstanding Technical Achievement Award, Factsheets, 2023
• IBM Corporate Award, Trustworthy AI, 2021
• 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 - present
• 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

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

**Program Committees **

• 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