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 existent 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" framework is a promising approach to analyse participants' thought processes in understanding, providing categories for both rational and emotional understanding. We further introduce the "dialogue" modality as a valid explanation approach to increase participant engagement and interaction with the "explainer", allowing for more insight into their understanding in the process. Our analysis further suggests that individuality in understanding affects participants' perceptions of algorithmic fairness, demonstrating the interdependence 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（ADM）系统影响，算法的伦理原则正日益凸显其重要性。理解这些系统的运作机制，有助于利益相关者做出明智决策，并评估这些系统对伦理价值的遵循程度。解释是构建理解的有效途径，但当前可解释人工智能（XAI）研究并未充分考虑关于理解形成与评估的既有理论。本研究旨在通过开展一项包含30名参与者（包括用户及受影响利益相关者）的定性任务型研究，深化对理解过程的认识。我们采用三种解释模态（文本、对话及交互式），向参与者解释一个“高风险”ADM系统，并运用Wiggins与McTighe提出的“理解六维度”框架，通过归纳与演绎方法分析参与者反馈。研究结果表明，“六维度”框架是分析参与者理解思维过程的有效方法，可提供理性与情感理解的双重分类维度。我们进一步提出将“对话”模态作为有效的解释方法，以增强参与者与“解释者”的互动性，从而更深入地洞察其理解过程。分析还表明，个体理解差异会影响参与者对算法公平性的感知，这印证了先前研究提出的理解与ADM评估之间的相互依存关系。我们认为，借鉴“六维度”等学习与理解理论，结合多样化解释模态，可引导XAI研究更好地适配个体学习过程，从而促进对ADM系统伦理价值的评估。