A Human-Centered Methodology for Creating AI FactSheets
- 2021
- IEEE Data Eng. Bull.
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
Invited Talks/Panels/Interviews
Publications
Assessing and implementing trustworthy AI across multiple dimensions (Book Chapter) in Ethics in Online AI-Based Systems, Elsevier, April, 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, 2024
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
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Awards, Services, and Other Activities
Tutorials and Courses
Program Committees
2024: FAccT 2024, Big Data 2024, GenAICHI 2024
2023: FAccT 2023, Big Data 2023
2022: AIES 2022, CHI'22 LBW (reviewer)
2021: AIES 2021
2017: CASCON'17
2016: PLDI'16 EPC, ISMM'16 ERC, ICPP'16
2015: LCPC'15, PLDI'15 ERC
2013: SAC'13 (PL Track)_, _ICPE'13, MUSEPAT'13, CASCON'13
2011: X10 Workshop at PLDI, SAC'11 (PL Track)
2010: ASPLOS'10, PLDI'10 ERC, IWMSE'10, CASCON'10, SAC'10 (PL Track)
2008: IISWC''08, CASCON'08, First Workshop on Programming Language Curricula
2007: WDDD 2007
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