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Algorithmic decision-making using rankings— prevalent in areas from hiring and bail to university admissions— raises concerns of potential bias. In this paper, we explore the alignment between people’s perceptions of fairness and two popular fairness metrics designed for rankings. In a crowdsourced experiment with 480 participants, people rated the perceived fairness of a hypothetical scholarship distribution scenario. Results suggest a strong inclination towards relying on explicit score values. There is also evidence of people’s preference for one fairness metric, NDKL, over the other metric, ARP. Qualitative results paint a more complex picture: some participants endorse meritocratic award schemes and express concerns about fairness metrics being used to modify rankings; while other participants acknowledge socio-economic factors in score-based rankings as justification for adjusting rankings. In summary, we find that operationalizing algorithmic fairness in practice is a balancing act between mitigating harms towards marginalized groups and societal conventions of leveraging traditional performance scores such as grades in decision-making contexts. 

For applications where multiple stakeholders provide recommendations, a fair consensus ranking must not only ensure that the preferences of rankers are well represented, but must also mitigate disadvantages among socio-demographic groups in the final result. However, there is little empirical guidance on the value or challenges of visualizing and integrating fairness metrics and algorithms into human-in-the-loop systems to aid decision-makers. In this work, we design a study to analyze the effectiveness of integrating such fairness metrics-based visualization and algorithms. We explore this through a task-based crowdsourced experiment comparing an interactive visualization system for constructing consensus rankings, ConsensusFuse, with a similar system that includes visual encodings of fairness metrics and fair-rank generation algorithms, FairFuse. We analyze the measure of fairness, agreement of rankers’ decisions, and user interactions in constructing the fair consensus ranking across these two systems. In our study with 200 participants, results suggest that providing these fairness-oriented support features nudges users to align their decision with the fairness metrics while minimizing the tedious process of manually having to amend the consensus ranking. We discuss the implications of these results for the design of next-generation fairness oriented-systems and along with emerging directions for future research. 

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/.