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Although systematic biases in decision-making are widely documented, the waysin which they emerge from different sources is less understood. We present acontrolled experimental platform to study gender bias in hiring by decouplingthe effect of world distribution (the gender breakdown of candidates in aspecific profession) from bias in human decision-making. We explore theeffectiveness of \textit{representation criteria}, fixed proportional displayof candidates, as an intervention strategy for mitigation of gender bias byconducting experiments measuring human decision-makers' rankings for who theywould recommend as potential hires. Experiments across professions with varyinggender proportions show that balancing gender representation in candidateslates can correct biases for some professions where the world distribution isskewed, although doing so has no impact on other professions where humanpersistent preferences are at play. We show that the gender of thedecision-maker, complexity of the decision-making task and over- andunder-representation of genders in the candidate slate can all impact the finaldecision. By decoupling sources of bias, we can better isolate strategies forbias mitigation in human-in-the-loop systems.

What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring