Hiring in the Digital Society Content and Consequences of Gender and Age Stereotypes in Job Advertisements

The dissertation at hand empirically investigates inequities in algorithmically-aided hiring. Taking an interdisciplinary approach with communication science at its heart, it traces how inequities enter the hiring and recruitment (HR) process through the use of gender- and age-stereotypical warmth and competence frames in sourcing material, i.e., job advertisements. Chapter 2, the first empirical chapter situated in the sourcing phase of the HR process, demonstrates that stereotypical frames present in online job advertisements mirror stereotypes of social groups dominant in said domain. Chapter 3, also situated in the sourcing phase, shows that online platforms deliver job advertisements along the same stereotypical lines observed in the previous chapter, however, country-level individualistic and egalitarian norms alter delivery patterns. The final empirical chapter, Chapter 4, is situated in the selection phase of the HR process and shows that candidate selection is not fully dictated by AI recommendations, however, these recommendations do influence more subtle candidate fit assessments, i.e., candidate ratings. On the whole, the findings highlight the contextuality of social group stereotypes in the occupational domain and the breadth of considerations that go into understanding, defining, and mitigating algorithmic bias in hiring. For practitioners, stakeholders, and policy makers interested in fair distribution of work opportunities, the findings point out areas where efforts can be best spent in order to foster hiring equity in a digital society.