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

使用算法排序和可解释的 AI 方法实现透明的排名解释，通过可视化解释模型拟合和属性对排名的影响来探索和解释复杂的多属性排名数据的子集和分组，在不了解黑盒排名模型的情况下，帮助具备数据科学训练的最终用户透明地推理排名的全局和局部行为。