Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

Picture for Michael Hind

A Methodology for Creating AI FactSheets


Jun 28, 2020
John Richards, David Piorkowski, Michael Hind, Stephanie Houde, Aleksandra Mojsilović

* 18 pages 

  Access Paper or Ask Questions

Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness


Jan 13, 2020
Michael Hind, Dennis Wei, Yunfeng Zhang


  Access Paper or Ask Questions

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


Sep 14, 2019
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang


  Access Paper or Ask Questions

Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning


Jun 05, 2019
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilović

* presented at 2019 ICML Workshop on Human in the Loop Learning (HILL 2019), Long Beach, USA. arXiv admin note: substantial text overlap with arXiv:1805.11648 

  Access Paper or Ask Questions

TED: Teaching AI to Explain its Decisions


Nov 12, 2018
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic

* This article leverages some content from arXiv:1805.11648 

  Access Paper or Ask Questions

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias


Oct 03, 2018
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, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang

* 20 pages 

  Access Paper or Ask Questions

Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach


Sep 22, 2018
Ravi Kiran Raman, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K. Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney

* 16 pages, 8 figures 

  Access Paper or Ask Questions

Teaching Meaningful Explanations


Sep 11, 2018
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic

* 9 pages 

  Access Paper or Ask Questions

Increasing Trust in AI Services through Supplier's Declarations of Conformity


Aug 22, 2018
Michael Hind, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, Kush R. Varshney


  Access Paper or Ask Questions

Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images


Aug 01, 2018
Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogerio Feris, John R. Smith

* Presented at MICCAI 2018, Workshop on Interpretability of Machine Intelligence in Medical Image Computing (IMIMIC): https://imimic.bitbucket.io 

  Access Paper or Ask Questions