III: Small: Collaborative Research: Towards Interpretable Machine Learning

这个研究项目调查了机器学习算法的设计和开发，这些算法可以做出人类可以解释的决策。随着机器学习模型的预测越来越多地用于做出具有关键后果的决策（例如，在医学或经济学中），决策者理解这些预测背后的基本原理非常重要。该项目通过三个关键属性定义可解释的算法；简单性：非机器学习专家的用户可以直观地理解；可验证性：输入特征和模型输出之间的明确关系；可操作性：对于给定的输入和期望的输出，用户应该能够识别将模型预测转换为期望输出的输入特征的变化。该项目研究了如何设计支持简单性和可验证性的距离度量，以及识别输入变化到变化输出的算法。该项目将在医疗背景下进行评估，解决早期发现有突然恶化危险的住院病人的问题。这项工作建立在很容易理解的k-Nearest-Neighbor分类器的基础上，它本身似乎提供了简单性和可验证性。挑战在于高维度，例如，当用于文档分类时；差异在更多的维度上蔓延，超出了人类的理解范围。该项目使用新颖的降维方法来创建可解释和准确的差异度量。将探索显示这些数据的可视化技术，包括支持更复杂的分类方法（如集成）的技术。该项目研究了在机器学习算法中提供可操作性的新方法，通过识别可以真正改变实体的类成员资格的变化——这是一个最近被认为非常困难的问题。第二个结果将是分类器鲁棒性的改进，因为改变类隶属度的小变化是非鲁棒性的很好指示。

This research project investigates the design and development of machine learning algorithms that make decisions that are interpretable by humans. As predictions of machine learning models are increasingly used in making decisions with critical consequences (e.g., in medicine or economics), it is important that decision makers understand the rationale behind these predictions. The project defines interpretable algorithms through three key properties; Simplicity: intuitively comprehensible by users who are not experts in machine learning, Verifiability: a clear relationship between input features and model output, and Actionability: For a given input and desired output, the user should be able to identify changes to the input features that transform the model prediction to the desired output. The project investigates how to design distance metrics supporting simplicity and verifiability, as well as algorithms to identify input changes to change outputs. The project will be evaluated in a medical context, addressing the problem of early detection of hospital patients at risk of sudden deterioration.This work builds on the well-understood k-Nearest-Neighbor classifier, which would inherently seem to provide simplicity and verifiability. The challenge is in high dimensions, e.g., when used for document classification; differences are spread across more dimensions than are humanly comprehensible. The project uses novel dimensionality reduction approaches to create dissimilarity metrics that are interpretable and accurate. Visualization techniques to present this data will be explored, including techniques supporting more complex classification approaches such as ensembles. The project investigates novel methods for delivering actionability in machine learning algorithms by identifying changes that can truly transform an entity's class membership - a problem that has recently been identified as surprisingly difficult. A secondary outcome will be improvements in classifier robustness, as small changes that change class membership are a good indication of non-robustness.