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'Explaining' machine learning reveals policy challenges

Coyle, D and Weller, A (2020) 'Explaining' machine learning reveals policy challenges. Science, 368. pp. 1433-1434. (Unpublished)

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There is a growing demand to be able to “explain” machine learning (ML) systems’ decisions and actions to human users, par-ticularly when used in contexts where deci-sions have substantial implications for those affected and where there is a requirement for political accountability or legal compli-ance. Explainability is often discussed as a technical challenge in designing ML systems and decision procedures, to improve under-standing of what is typically a “black box” phenomenon. But some of the most difficult challenges are non-technical and raise ques-tions about the broader accountability of organizations using ML in their decision-making.

Item Type: Article
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 12 Jun 2020 20:03
Last Modified: 09 Sep 2021 01:39
DOI: 10.1126/science.aba9647