“Explaining” machine learning reveals policy challenges-Reference-Cited by-同舟云学术

“Explaining” machine learning reveals policy challenges

Published:2020-06-26 Issue:6498 Volume:368 Page:1433-1434
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Coyle Diane¹,Weller Adrian²³

Affiliation:

1. Bennett Institute for Public Policy, University of Cambridge, Cambridge, UK.

2. University of Cambridge, UK.

3. The Alan Turing Institute, London, UK.

Abstract

The need to make objectives explicit may expose policy trade-offs that had previously been implicit and obscured

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference15 articles.

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2. R. Richardson, J. Schultz, K. Crawford, New York Univ. Law Rev. 192, 204 (2019).

3. Z. Lipton The mythos of model interpretability (2017); https://arxiv.org/pdf/1606.03490.pdf.

4. T. Miller Explanation in artificial intelligence: Insights from the social sciences (2018); https://arxiv.org/pdf/1706.07269.pdf.

5. P. Madumal T. Miller L. Sonenberg F. Vetere A grounded interaction protocol for explainable artificial intelligence (2019); https://arxiv.org/pdf/1903.02409.pdf.

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