可解释的机器学习--简史、现状与挑战
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
摘要：
我们介绍了可解释机器学习（IML）领域的简史，概述了最先进的解释方法，并讨论了挑战。近年来，IML的研究蓬勃发展。尽管这个领域很年轻，但它在回归建模和基于规则的机器学习方面有着200多年的历史，始于20世纪60年代。最近，提出了许多新的IML方法，其中许多是模型不可知的，但也有特定于深度学习和基于树的集成的解释技术。IML方法要么直接分析模型组件，研究对输入扰动的敏感性，要么分析ML模型的局部或全局代理近似。该领域接近准备就绪和稳定的状态，许多方法不仅在研究中提出，而且在开源软件中实现。但IML仍然面临着许多重要的挑战，如处理相关特征，因果解释和不确定性估计，这些问题需要解决才能成功地应用于科学问题。另一个挑战是缺乏一个严格的可解释性的定义，这是由社区接受。为了应对挑战并推动该领域的发展，我们迫切需要回顾我们在统计和（基于规则的）ML中可解释的数据驱动建模的根源，但也要考虑其他领域，如敏感性分析，因果推断和社会科学。
英文摘要：
We present a brief history of the field of interpretable machine learning (IML), give an overview of state-of-the-art interpretation methods, and discuss challenges. Research in IML has boomed in recent years. As young as the field is, it has over 200 years old roots in regression modeling and rule-based machine learning, starting in the 1960s. Recently, many new IML methods have been proposed, many of them model-agnostic, but also interpretation techniques specific to deep learning and tree-based ensembles. IML methods either directly analyze model components, study sensitivity to input perturbations, or analyze local or global surrogate approximations of the ML model. The field approaches a state of readiness and stability, with many methods not only proposed in research, but also implemented in open-source software. But many important challenges remain for IML, such as dealing with dependent features, causal interpretation, and uncertainty estimation, which need to be resolved for its successful application to scientific problems. A further challenge is a missing rigorous definition of interpretability, which is accepted by the community. To address the challenges and advance the field, we urge to recall our roots of interpretable, data-driven modeling in statistics and (rule-based) ML, but also to consider other areas such as sensitivity analysis, causal inference, and the social sciences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
