Afnan, TanishaRabaan, HawraJones, Kyle M. L.Dombrowski, Lynn2023-04-262023-04-262021-10Afnan, T., Rabaan, H., Jones, K. M. L., & Dombrowski, L. (2021). Asymmetries in Online Job-Seeking: A Case Study of Muslim-American Women. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 404:1-404:29. https://doi.org/10.1145/34795482573-0142https://hdl.handle.net/1805/32653As job-seeking and recruiting processes transition into digital spaces, concerns about hiring discrimination in online spaces have developed. Historically, women of color, particularly those with marginalized religious identities, have more challenges in securing employment. We conducted 20 semi-structured interviews with Muslim-American women of color who had used online job platforms in the past two years to understand how they perceive digital hiring tools to be used in practice, how they navigate the US job market, and how hiring discrimination as a phenomenon is thought to relate to their intersecting social identities. Our findings allowed us to identify three major categories of asymmetries (i.e., the relationship between the computing algorithms' structures and their users' experiences): (1) process asymmetries, which is the lack of transparency in data collection processes of job applications; (2) information asymmetries, which refers to the asymmetry in data availability during online job-seeking; and (3) legacy asymmetries, which explains the cultural and historical factors impacting marginalized job applicants. We discuss design implications to support job seekers in identifying and securing positive employment outcomes.enPublisher Policyalgorithmic fairnessonline job seekingdiscriminationArticle