This empirical study proposes a novel methodology to measure users' perceived
trust in an Explainable Artificial Intelligence (XAI) model. To do so, users'
mental models are elicited using Fuzzy Cognitive Maps (FCMs). First, we exploit
an interpretable Machine Learning (ML) model to classify suspected COVID-19
patients into positive or negative cases. Then, Medical Experts' (MEs) conduct
a diagnostic decision-making task based on their knowledge and then prediction
and interpretations provided by the XAI model. In order to evaluate the impact
of interpretations on perceived trust, explanation satisfaction attributes are
rated by MEs through a survey. Then, they are considered as FCM's concepts to
determine their influences on each other and, ultimately, on the perceived
trust. Moreover, to consider MEs' mental subjectivity, fuzzy linguistic
variables are used to determine the strength of influences. After reaching the
steady state of FCMs, a quantified value is obtained to measure the perceived
trust of each ME. The results show that the quantified values can determine
whether MEs trust or distrust the XAI model. We analyze this behavior by
comparing the quantified values with MEs' performance in completing diagnostic
tasks.

通过模糊认知图确定解释性人工智能模型信任度，评估不同解释满意度对信任度的影响，并结合医学专家在 COVID-19 诊断任务中的表现来分析信任行为。

通过引导认知模型来测量在 XAI 辅助决策中的感知信任

Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a  Mental Model

Natural Language Generation tools, such as chatbots that can generate
human-like conversational text, are becoming more common both for personal and
professional use. However, there are concerns about their trustworthiness and
ethical implications. The paper addresses the problem of understanding how
different users (e.g., linguists, engineers) perceive and adopt these tools and
their perception of machine-generated text quality. It also discusses the
perceived advantages and limitations of Natural Language Generation tools, as
well as users' beliefs on governance strategies. The main findings of this
study include the impact of users' field and level of expertise on the
perceived trust and adoption of Natural Language Generation tools, the users'
assessment of the accuracy, fluency, and potential biases of machine-generated
text in comparison to human-written text, and an analysis of the advantages and
ethical risks associated with these tools as identified by the participants.
Moreover, this paper discusses the potential implications of these findings for
enhancing the AI development process. The paper sheds light on how different
user characteristics shape their beliefs on the quality and overall
trustworthiness of machine-generated text. Furthermore, it examines the
benefits and risks of these tools from the perspectives of different users.

本文研究了自然语言生成工具，如生成人类对话文本的聊天机器人，其可信度和伦理影响的问题，讨论了不同用户对这些工具的感知和采用方式以及机器生成文本质量的感知优缺点，还分析了参与者确定的这些工具的优点和伦理风险，并探讨了这些发现对于提升 AI 开发过程的潜在影响。

自然语言生成器的可信度

Perceived Trustworthiness of Natural Language Generators

This paper presents an argument for why we are not measuring trust
sufficiently in explainability, interpretability, and transparency research.
Most studies ask participants to complete a trust scale to rate their trust of
a model that has been explained/interpreted. If the trust is increased, we
consider this a positive. However, there are two issues with this. First, we
usually have no way of knowing whether participants should trust the model.
Trust should surely decrease if a model is of poor quality. Second, these
scales measure perceived trust rather than demonstrated trust. This paper
showcases three methods that do a good job at measuring perceived and
demonstrated trust. It is intended to be starting point for discussion on this
topic, rather than to be the final say. The author invites critique and
discussion.

本文探讨了当前解释性、可理解性和透明度研究中未能充分衡量信任的原因及其实验设计中存在的问题，并提出了三种方法来较好地衡量感知的信任和实际的信任，旨在引发对该主题的讨论、批评和探究。