AI and Inequality in Hiring and Recruiting: A Field Scan

Dinika, Adio-Adet; Sloane, Mona

Bibtex export

@incollection{ Dinika2023,
 title = {AI and Inequality in Hiring and Recruiting: A Field Scan},
 author = {Dinika, Adio-Adet and Sloane, Mona},
 year = {2023},
 booktitle = {Proceedings of the Weizenbaum Conference 2023: AI, Big Data, Social Media, and People on the Move},
 pages = {1-13},
 address = {Berlin},
 publisher = {Weizenbaum Institute for the Networked Society - The German Internet Institute},
 doi = {https://doi.org/10.34669/wi.cp/5.3},
 abstract = {This paper provides a field scan of scholarly work on AI and hiring. It addresses the issue that there still is no comprehensive understanding of how technical, social science, and managerial scholarships around AI bias, recruiting, and inequality in the labor market intersect, particularly vis-à-vis the STEM field. It reports on a semi-systematic literature review and identifies three overlapping meta themes: productivity, gender, and AI bias. It critically discusses these themes and makes recommendations for future work.},
 keywords = {künstliche Intelligenz; artificial intelligence; Ungleichheit; inequality; Gender; gender; Arbeitsmarkt; labor market; Arbeit; labor; Diskriminierung; discrimination; Personalmanagement; human resource management; Rekrutierung; recruitment}}