  Fair consensus building combines the preferences of multiple rankers into a
single consensus ranking, while ensuring any group defined by a protected
attribute (such as race or gender) is not disadvantaged compared to other
groups. Manually generating a fair consensus ranking is time-consuming and
impractical -- even for a fairly small number of candidates. While algorithmic
approaches for auditing and generating fair consensus rankings have been
developed, these have not been operationalized in interactive systems. To
bridge this gap, we introduce FairFuse, a visualization system for generating,
analyzing, and auditing fair consensus rankings. We construct a data model
which includes base rankings entered by rankers, augmented with measures of
group fairness, and algorithms for generating consensus rankings with varying
degrees of fairness. We design novel visualizations that encode these measures
in a parallel-coordinates style rank visualization, with interactions for
generating and exploring fair consensus rankings. We describe use cases in
which FairFuse supports a decision-maker in ranking scenarios in which fairness
is important, and discuss emerging challenges for future efforts supporting
fairness-oriented rank analysis. Code and demo videos available at
https://osf.io/hd639/.
