Interpretable Machine Learning

Author:

Chen Valerie1,Li Jeffrey2,Kim Joon Sik1,Plumb Gregory1,Talwalkar Ameet1

Affiliation:

1. Carnegie Mellon University

2. University of Washington

Abstract

The emergence of machine learning as a society-changing technology in the past decade has triggered concerns about people's inability to understand the reasoning of increasingly complex models. The field of IML (interpretable machine learning) grew out of these concerns, with the goal of empowering various stakeholders to tackle use cases, such as building trust in models, performing model debugging, and generally informing real human decision-making.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference24 articles.

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