可解释机器学习

Q3 Computer Science

Queue Pub Date : 2021-12-31 DOI:10.1145/3511299

Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar

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引用次数: 12

摘要

过去十年，机器学习作为一种改变社会的技术的出现，引发了人们对人们无法理解日益复杂的模型的推理的担忧。IML(可解释机器学习)领域从这些关注中发展而来，其目标是使各种涉众能够处理用例，例如在模型中建立信任，执行模型调试，以及通常为真实的人类决策提供信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Interpretable Machine Learning

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.

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来源期刊

Queue Computer Science-Computer Science (all)

CiteScore

1.80

自引率

0.00%

发文量