Explainable machine learning in deployment-Reference-Cited by-同舟云学术

Explainable machine learning in deployment

Published:2020-01-22 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
language:
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Author:

Bhatt Umang¹,Xiang Alice²,Sharma Shubham³,Weller Adrian⁴,Taly Ankur⁵,Jia Yunhan⁶,Ghosh Joydeep⁷,Puri Ruchir⁸,Moura José M. F.⁹,Eckersley Peter²

Affiliation:

1. Carnegie Mellon University and Partnership on AI and University of Cambridge and Leverhulme CFI

2. Partnership on AI

3. University of Texas at Austin

4. University of Cambridge and Leverhulme CFI and The Alan Turing Institute

5. Fiddler Labs

6. Baidu

7. University of Texas at Austin and CognitiveScale

8. IBM Research

9. Carnegie Mellon University

Funder

David MacKay Newton research fellowship at Darwin College

The Alan Turing Institute

DeepMind via the Leverhulme Centre for the Future of Intelligence (CFI)

Partnership on AI research fellowship

Leverhulme Trust via CFI

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3351095.3375624

Reference71 articles.

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2. 2019. Our approach: Microsoft AI principles. https://www.microsoft.com/en-us/ai/our-approach-to-ai 2019. Our approach: Microsoft AI principles. https://www.microsoft.com/en-us/ai/our-approach-to-ai

3. Sarah Adel Bargal Andrea Zunino Donghyun Kim Jianming Zhang Vittorio Murino and Stan Sclaroff. 2018. Excitation backprop for RNNs. In 'Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition'. 1440--1449. Sarah Adel Bargal Andrea Zunino Donghyun Kim Jianming Zhang Vittorio Murino and Stan Sclaroff. 2018. Excitation backprop for RNNs. In 'Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition'. 1440--1449.

4. Towards Algorithmic Experience

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