Information retrieval (IR) systems have become an integral part of our everyday lives. As search engines, recommender systems, and conversational agents are employed across various domains from recreational search to clinical decision support, there is an increasing need for transparent and explainable systems to guarantee accountable, fair, and unbiased results. Despite many recent advances towards explainable AI and IR techniques, there is no consensus on what it means for a system to be explainable. Although a growing body of literature suggests that explainability is comprised of multiple subfactors, virtually all existing approaches treat it as a singular notion. In this paper, we examine explainability in Web search systems, leveraging psychometrics and crowdsourcing to identify human-centered factors of explainability.

翻译：信息检索（IR）系统已成为我们日常生活中不可或缺的组成部分。随着搜索引擎、推荐系统和对话代理在从休闲搜索到临床决策支持等各个领域得到广泛应用，对透明且可解释系统的需求日益增长，以确保结果的可问责性、公平性与无偏性。尽管近期可解释人工智能与信息检索技术取得了诸多进展，但关于系统可解释性的定义尚未形成共识。虽然越来越多的文献表明可解释性包含多个子因素，但几乎所有现有方法都将其视为单一概念。本文利用心理测量学与众包方法，以人为中心识别网络搜索系统的可解释性因素，对其进行了实证研究。