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With the increasing pervasiveness of Artificial Intelligence (AI), manyvisual analytics tools have been proposed to examine fairness, but they mostlyfocus on data scientist users. Instead, tackling fairness must be inclusive andinvolve domain experts with specialized tools and workflows. Thus,domain-specific visualizations are needed for algorithmic fairness.Furthermore, while much work on AI fairness has focused on predictivedecisions, less has been done for fair allocation and planning, which requirehuman expertise and iterative design to integrate myriad constraints. Wepropose the Intelligible Fair Allocation (IF-Alloc) Framework that leveragesexplanations of causal attribution (Why), contrastive (Why Not) andcounterfactual reasoning (What If, How To) to aid domain experts to assess andalleviate unfairness in allocation problems. We apply the framework to fairurban planning for designing cities that provide equal access to amenities andbenefits for diverse resident types. Specifically, we propose an interactivevisual tool, Intelligible Fair City Planner (IF-City), to help urban plannersto perceive inequality across groups, identify and attribute sources ofinequality, and mitigate inequality with automatic allocation simulations andconstraint-satisfying recommendations. We demonstrate and evaluate the usageand usefulness of IF-City on a real neighborhood in New York City, US, withpracticing urban planners from multiple countries, and discuss generalizing ourfindings, application, and framework to other use cases and applications offair allocation.

IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality