验证了意大利版可解释性量表，用于评估AI解释质量
Quality of explanation of xAI from the prespective of Italian end-users: Italian version of System Causability Scale (SCS)
- 采用双向翻译法构建意大利语版解释质量量表
- 经验证删去1题，最终保留9题且用户理解度高
- 为意大利地区AI开发者提供可解释性评估工具
背景与目标：随着人工智能应用范围扩展至计算机科学以外领域，研究人员关注如何提供高质量的算法运作及数据提取解释。本研究旨在验证2020年发布的系统可解释性量表（SCS）意大利语版本（I-SCS）在可解释人工智能（xAI）中的适用性。方法：基于原始英文版，采用前向-反向翻译法确保准确性，并通过内容效度指数（CVR）计算和代表性终端用户认知访谈完成九步验证流程。结果：原问卷共10题，根据CVR低于0.49的标准，剔除第8题，最终形成包含9题的意大利语版本。代表性意大利终端用户完全理解题意。结论：该意大利语版本可应用于未来研究及xAI开发实践，用于衡量意大利文化背景下AI系统解释质量。
原文摘要 · Abstract (English)
Background and aim: Considering the scope of the application of artificial intelligence beyond the field of computer science, one of the concerns of researchers is to provide quality explanations about the functioning of algorithms based on artificial intelligence and the data extracted from it. The purpose of the present study is to validate the Italian version of system causability scale (I-SCS) to measure the quality of explanations provided in a xAI. Method: For this purpose, the English version, initially provided in 2020 in coordination with the main developer, was utilized. The forward-backward translation method was applied to ensure accuracy. Finally, these nine steps were completed by calculating the content validity index/ratio and conducting cognitive interviews with representative end users. Results: The original version of the questionnaire consisted of 10 questions. However, based on the obtained indexes (CVR below 0.49), one question (Question 8) was entirely removed. After completing the aforementioned steps, the Italian version contained 9 questions. The representative sample of Italian end users fully comprehended the meaning and content of the questions in the Italian version. Conclusion: The Italian version obtained in this study can be used in future research studies as well as in the field by xAI developers. This tool can be used to measure the quality of explanations provided for an xAI system in Italian culture.
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