Human-AI decision making is becoming increasingly ubiquitous, and explanations have been proposed to facilitate better Human-AI interactions. Recent research has investigated the positive impact of explanations on decision subjects' fairness perceptions in algorithmic decision-making. Despite these advances, most studies have captured the effect of explanations in isolation, considering explanations as ends in themselves, and reducing them to technical solutions provided through XAI methodologies. In this vision paper, we argue that the effect of explanations on fairness perceptions should rather be captured in relation to decision subjects' right to contest such decisions. Since contestable AI systems are open to human intervention throughout their lifecycle, contestability requires explanations that go beyond outcomes and also capture the rationales that led to the development and deployment of the algorithmic system in the first place. We refer to such explanations as process-centric explanations. In this work, we introduce the notion of process-centric explanations and describe some of the main challenges and research opportunities for generating and evaluating such explanations.

翻译：人机协同决策正变得日益普及，可解释性被视为促进人机高效互动的关键手段。近期研究关注了算法决策中可解释性对决策主体公平感知的积极影响，但多数研究仅将可解释性视为孤立的技术方案，通过XAI方法提供解释本身，而忽略了其与决策主体挑战决策的权利之间的关联。本文提出应将可解释性对公平感知的影响置于决策主体的可争议权框架下审视——由于可争议性要求AI系统全生命周期接受人类干预，这种特性需要超越结果层面的解释，以揭示算法系统最初开发与部署的根本逻辑。我们将此类解释定义为"面向过程的解释"，并系统阐述了生成与评估这类解释的核心挑战与研究机遇。