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A Methodology for Creating AI FactSheets

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

* 18 pages 

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

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


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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


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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 

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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 

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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 

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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 

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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 

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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


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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 

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