Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to assess the systems' adherence to ethical values. Explanations are a promising way to create understanding, but current explainable artificial intelligence (XAI) research does not always consider theories on how understanding is formed and evaluated. In this work, we aim to contribute to a better understanding of understanding by conducting a qualitative task-based study with 30 participants, including "users" and "affected stakeholders". We use three explanation modalities (textual, dialogue, and interactive) to explain a "high-risk" ADM system to participants and analyse their responses both inductively and deductively, using the "six facets of understanding" framework by Wiggins & McTighe. Our findings indicate that the "six facets" are a fruitful approach to analysing participants' understanding, highlighting processes such as "empathising" and "self-reflecting" as important parts of understanding. We further introduce the "dialogue" modality as a valid alternative to increase participant engagement in ADM explanations. Our analysis further suggests that individuality in understanding affects participants' perceptions of algorithmic fairness, confirming the link between understanding and ADM assessment that previous studies have outlined. We posit that drawing from theories on learning and understanding like the "six facets" and leveraging explanation modalities can guide XAI research to better suit explanations to learning processes of individuals and consequently enable their assessment of ethical values of ADM systems.

翻译：随着越来越多利益相关者受到"高风险"算法决策系统的影响，算法的伦理原则正日益重要。理解这些系统的运作方式，能够帮助利益相关者做出明智决策，并评估系统对伦理价值的遵循程度。解释是建立理解的一种有前景的方式，但当前的可解释人工智能研究并未充分借鉴关于理解如何形成与评估的理论。本研究旨在通过一项包含30名参与者（包括"用户"和"受影响利益相关者"）的定性任务型研究，促进对理解本质的深入认识。我们采用三种解释模态（文本、对话和交互）向参与者解释一个"高风险"算法决策系统，并运用Wiggins与McTighe的"理解六维度"框架，对参与者的反馈进行归纳与演绎分析。研究结果表明，"六维度"框架是分析参与者理解的有效工具，其中"共情"与"自我反思"等过程是理解的重要组成。我们进一步引入"对话"模态，作为提升参与者对算法决策解释参与度的有效替代方案。分析还揭示，理解特性会影响参与者对算法公平性的感知，印证了先前研究提出的理解与算法决策评估之间的关联。我们认为，借鉴"六维度"等学习与理解理论，并善用解释模态，可以引导可解释人工智能研究更好地适应个体学习过程，进而使利益相关者能够有效评估算法决策系统的伦理价值。