Michael Hind  Michael Hind photo         

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Distinguished Research Staff Member
IBM Research AI, IBM Thomas J. Watson Research Center, Yorktown Heights, NY USA
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Professional Associations:  ACM SIGPLAN

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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


2020

Best Practices for Insuring AI Algorithms
Phaedra Boinodiris and Michael Hind
Cognitive World, 2020
Abstract

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) pp. 21, 2020
Abstract

Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness
Michael Hind, Dennis Wei, Yunfeng Zhang
Workshop on Human Interpretability in Machine Learning (WHI), 2020
Abstract

A Methodology for Creating AI FactSheets
John Richards, David Piokowski, Michael Hind, Stephanie Houde, Aleksandra Mjosilovic
Technical Report, 2020
Abstract

AI Fairness: How to Measure and Reduce Unwanted Bias in Machine Learning
Trisha Mahoney, Kush R Varshney, and Michael Hind
O'Reilly, 2020

Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness
Michael Hind, Dennis Wei, Yunfeng Zhang
Technical Report, 2020
Abstract

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 2020, Late-Breaking Work
Abstract

Don't Generalize Until Your Model Does
Michael Hind
97 Things About Ethics Everyone in Data Science Should Know: Collective Wisdom from the Experts by Bill Franks, O'Reilly Media, Inc., 2020
Abstract


2019

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
Technical Report, 2019
Abstract

FactSheets: Increasing trust in AI services through supplier's declarations of conformity
M. Arnold, R. K. E. Bellamy, M. Hind, S. Houde, S. Mehta, A. Mojsilovic, R. Nair, K. Natesan Ramamurthy, A. Olteanu, D. Piorkowski, D. Reimer, J. Richards, J. Tsay, K. R. Varshney
IBM Journal of Research and Development 63(4/5), 2019
Abstract

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
R. K. E. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilovic, S. Nagar, K. Natesan Ramamurthy, J. Richards, D. Saha, P. Sattigeri, M. Singh, K. R. Varshney, Y. Zhang
IBM Journal of Research and Development 63(4/5), 2019
Abstract

Think Your Artificial Intelligence Software is Fair? Think Again.
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang.
IEEE Software 36(4), 76-80, 2019

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
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
2019
Abstract

Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
2019 ICML Workshop on Human in the Loop Learning (HILL 2019)
Abstract

Explaining explainable AI
Michael Hind
XRDS: Crossroads, The ACM Magazine for Students 25(3), ACM, 2019
Abstract

Promoting Distributed Trust in Machine Learning and Computational Simulation
Nelson Bore, Ravi Raman, Isaac M. Markus, Sekou L. Remy, Oliver Bent, Michael Hind, Eleftheria K. Pissadaki, Biplav Srivastava, Roman Vaculin, Kush R. Varshney, and Komminist Weldemariam
2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

A Scalable Blockchain Approach for Trusted Computation and Verifiable Simulation in Multi-Party Collaborations
avi Raman, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria Pissadaki, Nelson Bore, Roozbeh Daneshvar, Biplav Srivastava, and Kush R. Varshney
2019 IEEE International Conference on Blockchains and Cryptocurrency (ICBC 2019)

Constructing and Compressing Frames in Blockchain-based Verifiable Multi-party Computation
Ravi Raman, Kush R. Varshney, Roman Vaculin, Nelson Bore, Sekou L. Remy, Eleftheria K. Pissadaki, Michael Hin
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019)

TED: Teaching AI to Explain its Decisions
Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES-19), 2019
Abstract


2018

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasaanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
Technical Report, 2018
Abstract

FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity
Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Darrell Reimer, Alexandra Olteanu, David Piorkowski, Jason Tsay, Kush R. Varshney
Technical Report, 2018
Abstract

Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images
Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogerio Feris, John R. Smith
Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI, 2018
Abstract

Teaching Meaningful Explanations
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic
Technical Report, 2018
Abstract


2016

The Truth, The Whole Truth, and Nothing But the Truth: A Pragmatic Guide to Assessing Empirical Evaluations
S. M. Blackburn, A. Diwan, M. Hauswirth, P. F. Sweeney, J. N. Amaral, T. Brecht, L. Bulej, C. Click, L. Eeckhout, S. Fischmeister, D. Frampton, L. J. Hendren, M. Hind, A. L. Hosking, R. E. Jones, T. Kalibera, N. Keynes, N. Nystrom, and A. Zeller,
ACM TOPLAS, 2016

META: Middleware for Events, Transactions, and Analytics
M. Arnold, D. Grove, B. Herta, M. Hind, M. Hirzel, A. Iyengar, L. Mandel, V.A. Saraswat, A. Shinnar, J. Simeon, M. Takeuchi, O. Tardieu, W. Zhang
IBM Journal of Research and Development60, 2016
Abstract


2012

Can you trust your experimental results?
Stephen M. Blackburn, Amer Diwan, Matthias Hauswirth, Peter F. Sweeney, Jos Nelson Amaral, Vlastimil Babka, Walter Binder, Tim Brecht, Lubomr Bulej, Lieven Eeckhout, Sebastian Fischmeister, Daniel Frampton, Robin Garner, Andy Georges, Laurie J. Hendren,
Technical Report #1, February 15, 2012


2010

Preface: Commercial software for multicore systems
B Blainey, H Franke, M Hind
IBM Journal of Research and Development 54(5), 1--3, IBM, 2010


2008

Addressing the disconnect between the good and the popular
M Hind
ACM SIGPLAN Notices 43(11), 74--76, ACM, 2008


2007

Fast online pointer analysis
M Hirzel, D Von Dincklage, A Diwan, M Hind
ACM Transactions on Programming Languages and Systems ( …, 2007 - portal.acm.org

A Loop Correlation Technique to Improve Performance Auditing
J Lau, M Arnold, M Hind, B Calder
Proceedings of the 16th International Conference on Parallel …, 2007 - portal.acm.org

Using hpm-sampling to drive dynamic compilation
D Buytaert, A Georges, M Hind, M Arnold, L …
Proceedings of the 2007 OOPSLA conference, 2007 - portal.acm.org

Dynamic compilation: the benefits of early investing
P Kulkarni, M Arnold, M Hind
Proceedings of the 3rd international conference on Virtual execution environments, pp. 94--104, 2007


2006

Online phase detection algorithms
Priya Nagpurkar, Michael Hind, Chandra Krintz, Peter F Sweeney, VT Rajan
Code Generation and Optimization, 2006. CGO 2006. International Symposium on, pp. 13--pp

Online performance auditing: using hot optimizations without getting burned
J Lau, M Arnold, M Hind, B Calder
Proceedings of the 2006 ACM SIGPLAN conference on …, 2006 - portal.acm.org


2005

The Jikes research virtual machine project: building an open-source research community
Bowen Alpern, Stephen Augart, Stephen M Blackburn, Maira Butrico, Anthony Cocchi, Perry Cheng, Julian Dolby, Stephen Fink, David Grove, Michael Hind, Kathryn S McKinley, Mark Mergen, J Eliot B Moss, Ton Ngo, Vivek Sarkar
IBM System Journal 44(2), 399--417, IBM Corp., 2005

High-Level Real-Time Programming in Java
David F. Bacon, Perry Cheng, David Grove, Michael Hind, V. T. Rajan, Eran Yahav, Matthias Hauswirth, Christoph M. Kirsch, Daniel Spoonhower, Martin T. Vechev
Proceedings of the Fifth ACM International Conference on Embedded Software, pp. 68--78, ACM, 2005
Abstract   (slides)

A Survey of Adaptive Optimization in Virtual Machines
Matthew Arnold, Stephen J Fink, David Grove, Michael Hind, Peter F Sweeney
Proceedings of the IEEE 93(2), 449-466, IEEE, 2005
Special issue on Program Generation, Optimization, and Adaptation


2004

Pointer Analysis in the Presence of Dynamic Class Loading
M Hirzel, A Diwan, M Hind
LECTURE NOTES IN COMPUTER SCIENCE, 2004 - Springer

Vertical profiling: understanding the behavior of object-priented applications
M Hauswirth, Peter F Sweeney, A Diwan, Michael Hind
OOPSLA 2004 - ACN Conference on Object-Oriented Programming, Systems, Languages, and Applications

Using hardware performance monitors to understand the behavior of java applications
Peter F Sweeney, Matthias Hauswirth, Brendon Cahoon, Perry Cheng, Amer Diwan, David Grove, Michael Hind
VM'04: Proceedings of the 3rd conference on Virtual Machine Research And Technology Symposium, pp. 5--5, USENIX Association, 2004
Abstract

Architecture and policy for adaptive optimization in virtual machines
M Arnold, S Fink, D Grove, M Hind, P F Sweeney
Research Report23429, 2004


2003

The phase shift detection problem is non-monotonic
M Hind, V Rajan, P Sweeney
Technical Report, Technical Report RC23058, IBM, 2003

Phase shift detection: A problem classification
M Hind, V Rjan, P Sweeney
IBM Researh Report RC-22887, IBM TJ Watson, 2003


2002

Understanding the connectivity of heap objects
M Hirzel, J Henkel, A Diwan, M Hind
Proceedings of the 3rd international symposium on Memory …, 2002 - portal.acm.org

Online feedback-directed optimization of Java
M Arnold, M Hind, BG Ryder
ACM SIGPLAN Notices, 2002 - portal.acm.org


2001

Pointer analysis: haven't we solved this problem yet?
M Hind
Proceedings of the 2001 ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering, pp. 54--61

Evaluating the Effectiveness of Pointer Alias Analyses
Michael Hind, Anthony F Pioli
Science of Computer Programming, 2001


2000

NPIC—New Paltz interprocedural compiler
M Hind
ACM SIGSOFT Software Engineering Notes 25(1), 57--58, ACM, 2000

Traveling through Dakota: Experiences with an object-oriented program analysis system
M Hind, A Pioli
Technology of Object-Oriented Languages and Systems, 2000, pp. 49--60

Optimizing Java programs in the presence of exceptions
M Gupta, J D Choi, M Hind
ECOOP 2000—Object-Oriented Programming, 422--446, Springer

An Empirical Study of Selective Optimization
M Arnold, M Hind, BG Ryder
LECTURE NOTES IN COMPUTER SCIENCE, 2001 - Springer, 2000

Which pointer analysis should I use?
M Hind, A Pioli
Proceedings of the 2000 ACM SIGSOFT international symposium …, 2000 - portal.acm.org

Adaptive Optimization in the Jalapeno JVM: The Controllers Analytical Model
Matthew Arnold, Stephen Fink, David Grove, Michael Hind, Peter F Sweeney
The 3rd ACM Workshop on Feedback-Directed and Dynamic Optimization (FDDO-3),, pp. 15--19, 2000

Adaptive optimization in the Jalapeno JVM
Matthew Arnold, Stephen Fink, David Grove, Michael Hind, Peter F Sweeney
OOPSLA '00: Proceedings of the 15th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications, pp. 47--65, ACM, 2000
Abstract

The Jalapeno virtual machine
B Alpern, C R Attanasio, J J Barton, M G Burke, P Cheng, J D Choi, A Cocchi, S J Fink, D Grove, M Hind, others
IBM Systems Journal 39(1), 211, Armonk, NY: International Business Machines Corp., 2000


1999

Interprocedural Pointer Alias Analysis
M HIND, M BURKE, P CARINI, JD CHOI
ACM Transactions on Programming Languages and Systems, 1999 - portal.acm.org


Efficient and precise modeling of exceptions for the analysis of Java programs
Jong-Deok Choi, David Grove, Michael Hind, Vivek Sarkar
PASTE '99: Proceedings of the 1999 ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering, pp. 21--31, ACM
Abstract

The Jalapeno dynamic optimizing compiler for Java
Michael G. Burke, Jong-Deok Choi, Stephen Fink, David Grove, Michael Hind, Vivek Sarkar, Mauricio J. Serrano, V. C. Sreedhar, Harini Srinvasan, John Whaley
JAVA'99: Proceedings of the ACM 1999 confernece on Java Grande, pp. 129-141, ACM


1998


Assessing the Effects of Flow-Sensitivity on Pointer Alias Analyses
M Hind, A Pioli
LECTURE NOTES IN COMPUTER SCIENCE, 1998 - Springer


1997


Interprocedural Pointer Alias Analysis
M Burke, P Carini, JD Choi, M Hind
1997 - research.ibm.com


1996

Using regional conferences to mentor student development: a case study
M Hind, P Pfeiffer
ACM SIGPLAN Notices, 1996 - portal.acm.org


1995

Flow-Sensitive Type Analysis for C+
P R Carini, H Srinivasan, M Hind
1995 - Citeseer, Citeseer

Flow-sensitive interprocedural constant propagation
PR Carini, M Hind
ACM SIGPLAN Notices, 1995 - portal.acm.org


1994

An Empirical Study of Precise Interprocedural Array Analysis
M HIND, M BURKE, P CARINI, SAM MIDKIFF
Scientific Programming, 1994 - IOS Press

Flow-Insensitive Interprocedural Alias Analysis in the Presence of Pointers
M Burke, P Carini, JD Choi, M Hind
LECTURE NOTES IN COMPUTER SCIENCE, 1995 - Springer, 1994


1992

Loop Distribution with Multiple Exits (Extended Version)
BM Hsieh, M Hind, R Cytron
Technical Report RC 18229, IBM TJ Watson Research Center, 1992

Loop distribution with multiple exits
BM Hsieh, M Hind, R Cytron
Supercomputing'92. Proceedings, 1992 - ieeexplore.ieee.org

Interprocedural Array Analysis: How Much Precision Do We Need?
M Hind, P Carini, M Burke, S Midki
1992 - IBM TJ Watson Research Center


1991

Efficient loop-level parallelism in Ada
M Hind, E Schonberg
Proceedings of the conference on TRI-Ada'91: today's …, 1991 - portal.acm.org


1989

Automatic generation of DAG parallelism
R Cytron, M Hind, W Hsieh
Proceedings of the SIGPLAN'87 symposium on Interpreters and …, 1989 - portal.acm.org