Ranking schemes drive many real-world decisions, like, where to study, whom to hire, what to buy, etc. Many of these decisions often come with high consequences. For example, a university can be deemed less prestigious if not featured in a top-k list, and consumers might not even explore products that do not get recommended to buyers. At the heart of most of these decisions are opaque ranking schemes, which dictate the ordering of data entities, but their internal logic is inaccessible or proprietary. Drawing inferences about the ranking differences is like a guessing game to the stakeholders, like, the rankees (i.e., the entities who are ranked, like product companies) and the decision-makers (i.e., who use the rankings, like buyers). In this paper, we aim to enable transparency in ranking interpretation by using algorithmic rankers that learn from available data and by enabling human reasoning about the learned ranking differences using explainable AI (XAI) methods. To realize this aim, we leverage the exploration-explanation paradigm of human-data interaction to let human stakeholders explore subsets and groupings of complex multi-attribute ranking data using visual explanations of model fit and attribute influence on rankings. We realize this explanation paradigm for transparent ranking interpretation in TRIVEA, a visual analytic system that is fueled by: i) visualizations of model fit derived from algorithmic rankers that learn the associations between attributes and rankings from available data and ii) visual explanations derived from XAI methods that help abstract important patterns, like, the relative influence of attributes in different ranking ranges. Using TRIVEA, end users not trained in data science have the agency to transparently reason about the global and local behavior of the rankings without the need to open black-box ranking models and develop confidence in the resulting attribute-based inferences. We demonstrate the efficacy of TRIVEA using multiple usage scenarios and subjective feedback from researchers with diverse domain expertise. Keywords: Visual Analytics, Learning-to-Rank, Explainable ML, Ranking

翻译：排序机制驱动着许多现实世界中的决策，例如选择学习地点、雇佣对象、购买商品等。其中许多决策往往具有重大影响。例如，一所大学若未出现在前k名榜单中，可能被认为知名度较低；而消费者甚至可能不会探索那些未被推荐给买家的产品。这些决策的核心大多是不透明的排序机制，它们决定了数据实体的排序顺序，但其内部逻辑通常无法获取或属于商业机密。对于利益相关者（如被排序方，即被排序的实体，如产品公司；以及决策者，即使用排序结果的人，如买家）而言，推断排序差异的成因如同猜谜游戏。本文旨在利用能从可用数据中学习的算法排序器，并通过可解释人工智能（XAI）方法实现人类对所学排序差异的推理，从而提升排序解读的透明性。为实现这一目标，我们利用人-数据交互中的“探索-解释”范式，让人类利益相关者通过模型拟合度及属性对排序影响的可视化解释，探索复杂多属性排序数据的子集与分组。我们在TRIVEA中实现了这一透明排序解读的解释范式，该系统是一个可视化分析系统，由以下两部分驱动：i）源于算法排序器的模型拟合度可视化，这些排序器能从可用数据中学习属性与排序之间的关联；ii）源于XAI方法的可视化解释，有助于抽象出重要模式（例如不同排序区间中属性的相对影响）。通过TRIVEA，未经数据科学训练的最终用户无需打开黑箱排序模型，即可透明地推理排序的全局与局部行为，并对由此得出的基于属性的推论建立信心。我们通过多个使用场景和来自不同领域专家的主观反馈，展示了TRIVEA的有效性。关键词：可视化分析、排序学习、可解释机器学习、排序