Evaluating the efficiency of human-AI interactions is challenging, including
subjective and objective quality aspects. With the focus on the human
experience of the explanations, evaluations of explanation methods have become
mostly subjective, making comparative evaluations almost impossible and highly
linked to the individual user. However, it is commonly agreed that one aspect
of explanation quality is how effectively the user can detect if the
predictions are trustworthy and correct, i.e., if the explanations can increase
the user's appropriate trust in the model. This paper starts with the
definitions of appropriate trust from the literature. It compares the
definitions with model performance evaluation, showing the strong similarities
between appropriate trust and model performance evaluation. The paper's main
contribution is a novel approach to evaluating appropriate trust by taking
advantage of the likenesses between definitions. The paper offers several
straightforward evaluation methods for different aspects of user performance,
including suggesting a method for measuring uncertainty and appropriate trust
in regression.

通过比较定义和模型性能评估，本文提出了一种新颖的方法来评估适当的信任，包括针对用户表现的几种简单直接的评估方法，包括建议一种测量回归中不确定性和适当信任的方法。

关于适当信任的定义及其相关工具

On the Definition of Appropriate Trust and the Tools that Come with it

An important goal in the field of human-AI interaction is to help users more
appropriately trust AI systems' decisions. A situation in which the user may
particularly benefit from more appropriate trust is when the AI receives
anomalous input or provides anomalous output. To the best of our knowledge,
this is the first work towards understanding how anomaly alerts may contribute
to appropriate trust of AI. In a formative mixed-methods study with 4
radiologists and 4 other physicians, we explore how AI alerts for anomalous
input, very high and low confidence, and anomalous saliency-map explanations
affect users' experience with mockups of an AI clinical decision support system
(CDSS) for evaluating chest x-rays for pneumonia. We find evidence suggesting
that the four anomaly alerts are desired by non-radiologists, and the
high-confidence alerts are desired by both radiologists and non-radiologists.
In a follow-up user study, we investigate how high- and low-confidence alerts
affect the accuracy and thus appropriate trust of 33 radiologists working with
AI CDSS mockups. We observe that these alerts do not improve users' accuracy or
experience and discuss potential reasons why.

通过与放射科医生和其他医生进行的研究，我们发现非放射科医生希望四种异常预警，而放射科医生和非放射科医生都希望高置信度预警可以帮助 APPROPRIATE TRUST。然而，我们的随机对照试验显示，这些警报并不会提高用户的准确性或体验。

探究异常的模型输入和输出警报如何影响医疗决策

Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare

Many researchers motivate explainable AI with studies showing that human-AI
team performance on decision-making tasks improves when the AI explains its
recommendations. However, prior studies observed improvements from explanations
only when the AI, alone, outperformed both the human and the best team. Can
explanations help lead to complementary performance, where team accuracy is
higher than either the human or the AI working solo? We conduct mixed-method
user studies on three datasets, where an AI with accuracy comparable to humans
helps participants solve a task (explaining itself in some conditions). While
we observed complementary improvements from AI augmentation, they were not
increased by explanations. Rather, explanations increased the chance that
humans will accept the AI's recommendation, regardless of its correctness. Our
result poses new challenges for human-centered AI: Can we develop explanatory
approaches that encourage appropriate trust in AI, and therefore help generate
(or improve) complementary performance?

研究表明，仅当 AI 单独胜过人类和最佳团队时，AI 解释才能提高人类 - AI 团队在决策任务上的表现。本研究通过三个数据集上的混合法用户研究发现，AI 表现增强是有可能提高团队准确性的，但并非由于解释的作用，而是由于解释能提高人类对 AI 建议的接受率，进而鼓励恰当的信任和生成更好的绩效。