Fair Decision Making by Consensus: Interactive Bias Mitigation Technology

As the use of AI becomes ever more prevalent in socio-technical systems, decision makers frequently collaborate not only with each other, but also with automated technologies to make judgements that have real and lasting impact on other people's lives.

This present reality has serious implications for the equitable and fair treatment of historically disadvantaged groups, due to the potential interplay between implicit bias analysts may suffer from and algorithmic bias inadvertently embedded in AI systems.

Toward this aim, the AEQUITAS project investigates the application of contemporary notions of group fairness to the classic task of aggregating multiple rankings of candidates to derive an overall fair consensus decision. This technology will have impactful applications in domains from hiring, lending, to education, where decisions often made by committee with input from multiple decision makers must have unbiased outcomes.

KEYWORDS: Fair Rank Aggregation Processing; Bias Mitigation Visual Analytics; Consensus Building; Human-in-Loop Data Discovery.

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