Society's capacity for algorithmic problem-solving has never been greater. Artificial Intelligence is now applied across more domains than ever, a consequence of powerful abstractions, abundant data, and accessible software. As capabilities have expanded, so have risks, with models often deployed without fully understanding their potential impacts. Interpretable and interactive machine learning aims to make complex models more transparent and controllable, enhancing user agency. This review synthesizes key principles from the growing literature in this field. We first introduce precise vocabulary for discussing interpretability, like the distinction between glass box and explainable algorithms. We then explore connections to classical statistical and design principles, like parsimony and the gulfs of interaction. Basic explainability techniques -- including learned embeddings, integrated gradients, and concept bottlenecks -- are illustrated with a simple case study. We also review criteria for objectively evaluating interpretability approaches. Throughout, we underscore the importance of considering audience goals when designing interactive algorithmic systems. Finally, we outline open challenges and discuss the potential role of data science in addressing them. Code to reproduce all examples can be found at https://go.wisc.edu/3k1ewe.

翻译：社会解决算法问题的能力从未如此强大。如今，人工智能的应用领域比以往任何时候都更加广泛，这得益于强大的抽象能力、丰富的数据资源以及易获取的软件工具。随着能力的扩展，风险也随之增加——模型常常在未充分理解潜在影响的情况下被部署。可理解且可交互的机器学习旨在使复杂模型更加透明和可控，从而增强用户能动性。本文综述了该领域日益增长的研究文献中的关键原理。我们首先定义了用于讨论可解释性的精确术语，例如"玻璃箱"算法与可解释算法之间的区别。接着探讨了与经典统计学和设计原理（如简约性原则和交互鸿沟理论）的联系。通过一个简单案例研究，我们阐释了基本的可解释性技术——包括学习型嵌入、积分梯度和概念瓶颈。此外，我们还回顾了客观评估可解释性方法的标准。全文始终强调在设计交互式算法系统时考虑受众目标的重要性。最后，我们概述了开放性挑战，并讨论了数据科学在应对这些挑战中的潜在作用。所有示例的复现代码可访问 https://go.wisc.edu/3k1ewe 获取。