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Status-based theories of labor market inequality contend that, even when workers have identical qualifications and performance, employers evaluate them differently based on stereotypes about their status group. Gender and parenthood are status characteristics that affect decisions about hiring, pay, and promotion through stereotypes that mothers should not work, fathers should not take leave, and caregivers of either gender are less reliable, committed workers. We contend that family-leave laws mitigate these status effects by conveying a consensus that both men and women can legitimately combine work and family. An experiment testing this theory shows that, when the law is not salient, participants pay mothers (whether or not they take leave) and fathers who take leave less and rate them as less promotable than other identical workers. Participants also rate these employees as less competent, committed, and congenial than other identical workers. By contrast, when participants review family-leave laws before they evaluate employees, they treat mothers and caregivers no worse than other workers. Reviewing an organizational family-leave policy did not reduce the effects of stereotypes as much as reviewing formal law. These findings suggest that making law salient during workplace evaluations can reduce inequality through law’s expressive effects. 

Emerging methods for participatory algorithm design have proposed collecting and aggregating individual stakeholders’ preferences to create algorithmic systems that account for those stakeholders’ values. Drawing on two years of research across two public school districts in the United States, we study how families and school districts use students’ preferences for schools to meet their goals in the context of algorithmic student assignment systems. We find that the design of the preference language, i.e. the structure in which participants must express their needs and goals to the decision-maker, shapes the opportunities for meaningful participation. We define three properties of preference languages – expressiveness, cost, and collectivism – and discuss how these factors shape who is able to participate, and the extent to which they are able to effectively communicate their needs to the decision-maker. Reflecting on these findings, we offer implications and paths forward for researchers and practitioners who are considering applying a preference-based model for participation in algorithmic decision making.