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:随着越来越多的利益攸关方受到“高风险”算法决策系统(ADM)的影响,算法的道德原则越来越重要。理解这些系统如何运作使利益攸关方能够做出知情决定并评估系统遵守道德价值观的情况。解释是产生理解的一个很有希望的方法,但目前可解释的人工智能(XAI)研究并不总是考虑到关于如何形成和评估理解的理论。在这项工作中,我们的目标是通过与30个参与者,包括“用户”和“受影响的利益攸关方”开展基于任务的定性研究,促进更好地理解。我们进一步采用三种解释模式(文字、对话和互动)向参与者解释“高风险”ADM系统,并用感性分析他们的反应,同时使用Wiggins & McTighe的“理解的六方面”框架。我们的研究结果表明,“六个方面”是分析参与者理解的富有成果的方法,强调“了解”和“自我理解”等进程是了解的重要部分。我们进一步引入“对话”模式,作为提高投资者对ADMDMA的公平性理解认识的正确备选方案,我们从先前的学习和理解角度分析中可以看出,我们先前的对ADMDR解释的学习和理解的系统之间的理解可以影响。