As AI technology is increasingly applied to high-impact, high-risk domains,
there have been a number of new methods aimed at making AI models more human
interpretable. Despite the recent growth of interpretability work, there is a
lack of systematic evaluation of proposed techniques. In this work, we
introduce HIVE (Human Interpretability of Visual Explanations), a novel human
evaluation framework that assesses the utility of explanations to human users
in AI-assisted decision making scenarios, and enables falsifiable hypothesis
testing, cross-method comparison, and human-centered evaluation of visual
interpretability methods. To the best of our knowledge, this is the first work
of its kind. Using HIVE, we conduct IRB-approved human studies with nearly 1000
participants and evaluate four methods that represent the diversity of computer
vision interpretability works: GradCAM, BagNet, ProtoPNet, and ProtoTree. Our
results suggest that explanations engender human trust, even for incorrect
predictions, yet are not distinct enough for users to distinguish between
correct and incorrect predictions. We open-source HIVE to enable future studies
and encourage more human-centered approaches to interpretability research.

本研究提出了针对人类用户视觉解释的人类解释性评估框架 HIVE，通过对四种不同计算机视觉解释方法的评估，结果表明解释可以引起人类的信任，但人们难以区分解释的正确性，该框架开源以便未来研究和鼓励更多的以人为中心的解释性研究。

HIVE: 评估视觉解释的人类可解释性

HIVE: Evaluating the Human Interpretability of Visual Explanations

In recent years, the field of explainable AI (XAI) has produced a vast
collection of algorithms, providing a useful toolbox for researchers and
practitioners to build XAI applications. With the rich application
opportunities, explainability is believed to have moved beyond a demand by data
scientists or researchers to comprehend the models they develop, to an
essential requirement for people to trust and adopt AI deployed in numerous
domains. However, explainability is an inherently human-centric property and
the field is starting to embrace human-centered approaches. Human-computer
interaction (HCI) research and user experience (UX) design in this area are
becoming increasingly important. In this chapter, we begin with a high-level
overview of the technical landscape of XAI algorithms, then selectively survey
our own and other recent HCI works that take human-centered approaches to
design, evaluate, and provide conceptual and methodological tools for XAI. We
ask the question "what are human-centered approaches doing for XAI" and
highlight three roles that they play in shaping XAI technologies by helping
navigate, assess and expand the XAI toolbox: to drive technical choices by
users' explainability needs, to uncover pitfalls of existing XAI methods and
inform new methods, and to provide conceptual frameworks for human-compatible
XAI.

本文介绍了可解释 AI 的技术领域以及人们对人机交互和用户体验设计日益重视，在 XAI 的设计、评估、概念和方法论工具方面提供人本方法的作用。本文旨在提供人本的 XAI 设计，并帮助传达和扩展已有的 XAI 工具箱。