论文标题
解释机器学习模型的预测：算法，用户和教学法
Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy
论文作者
论文摘要
由于算法预测对人类的影响增加，模型解释性已成为机器学习（ML）的重要问题。解释不仅可以帮助用户了解为什么ML模型做出某些预测，还可以帮助用户了解这些预测如何更改。在本文中，我们从三个有利位置：算法，用户和教学法中研究了ML模型的解释性，并为解释性问题贡献了一些新颖的解决方案。
Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users understand not only why ML models make certain predictions, but also how these predictions can be changed. In this thesis, we examine the explainability of ML models from three vantage points: algorithms, users, and pedagogy, and contribute several novel solutions to the explainability problem.
