Measuring Bias in Job Recommender Systems: Auditing the Algorithms.

Autor/inn/en	Zhang, Shuo; Kuhn, Peter J.
Institution	National Bureau of Economic Research
Titel	Measuring Bias in Job Recommender Systems: Auditing the Algorithms. Gefälligkeitsübersetzung: Messung von Verzerrungen in Job-Empfehlungssystemen: Überprüfung der Algorithmen.
Quelle	Cambridge, Mass (2024), 127 S. PDF als Volltext kostenfreie Datei (1); PDF als Volltext kostenfreie Datei (2) Link als defekt meldenVerfügbarkeit
Reihe	NBER working paper / National Bureau of Economic Research. 32889
Sprache	englisch
Dokumenttyp	online; Monografie
Schlagwörter	Stereotyp; Frau; Algorithmus; Diskriminierung; Einkommensunterschied; Unternehmensgröße; Arbeitsuchender; Arbeitsvermittlung; Qualifikationsanforderung; Jobbörse; Arbeitspapier; Geschlechtsspezifik; Mann; China
Abstract	"We audit the job recommender algorithms used by four Chinese job boards by creating fictitious applicant profiles that differ only in their gender. Jobs recommended uniquely to the male and female profiles in a pair differ modestly in their observed characteristics, with female jobs advertising lower wages, requesting less experience, and coming from smaller firms. Much larger differences are observed in these ads' language, however, with women's jobs containing 0.58 standard deviations more stereotypically female content than men's. Using our experimental design, we can conclude that these gender gaps are generated primarily by content-based matching algorithms that use the worker's declared gender as a direct input. Action-based processes like item-based collaborative filtering and recruiters' reactions to workers' resumes contribute little to these gaps." The study refers to the period 2020-2021 (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2025/1