LISS panel Grant 2023. Authors: Huyen Nguyen (Universiteit Utrecht – Faculteit der Sociale Wetenschappen); Frank van Tubergen (Nederlands Interdisciplinair Demografisch Instituut); Valentina Di Stasio (Universiteit Utrecht – Faculteit der Sociale Wetenschappen).
Despite the growth of artificial intelligence (AI) in hiring, limited evidence exists on whether job candidates from different backgrounds self-present differently, and how they would adapt their self-presentation strategies to algorithmic hiring tools. While algorithmic systems hold promises to tackle persistent hiring inequality and discrimination, people’s self-confidence and perception of discrimination may shape their self-presentation strategies and their willingness to embrace such tools. Our study fills this gap with a vignette survey experiment to structurally map open-ended text answers to common interview questions with validated measures of self-confidence and perceived discrimination in the job market. Together with the linked LISS data on Work and Schooling, Personality, Politics and Values, we can map self-presentation strategies to social segregation and mobility patterns and inform policymakers of equitable AI recruitment systems.