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，算法伦理原则日益重要。理解这些系统的运作方式，使利益相关者能够做出明智决策，并评估系统对伦理价值的遵循程度。解释是一种有前景的促进理解的方法，但当前可解释人工智能研究并未充分考虑关于理解如何形成与评估的理论。本研究通过一项包含30名参与者（包括“用户”与“受影响利益相关者”）的定性任务型研究，旨在深化对“理解”的认识。我们采用三种解释模态（文本、对话、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“理解六侧面”框架，对参与者反馈进行归纳与演绎分析。研究结果表明，“六侧面”框架是分析参与者理解的有效路径，揭示了“共情”与“自我反思”等过程是理解的重要组成部分。我们进一步提出“对话”模态作为提升算法决策解释中参与者参与度的有效替代方案。分析还表明，理解的个体差异会影响参与者对算法公平性的感知，印证了先前研究指出的理解与算法决策评估之间的关联。我们认为，借鉴“六侧面”等学习与理解理论，并结合多元解释模态，可引导可解释人工智能研究根据个体学习过程优化解释方式，从而使其能够评估算法决策系统的伦理价值。