Interpretable Machine Learning

作者：Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar · 发表于：Queue · 年份：2021 · DOI：10.1145/3511299 · 被引用次数：541 · 研究领域：Explainable Artificial Intelligence (XAI)、Adversarial Robustness in Machine Learning、Machine Learning and Data Classification

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.