Shortlisted CVs
Shortlisted CVs
This repository accompanies the paper: A. Fabris, C. Rus, J. Saldivar, A. Gatzioura, A. Biega, C. Castillo. Does fair ranking lead to fair recruitment outcomes? A study of interventions, interfaces, and interactions. This project investigates whether fair ranking algorithms actually produce fair recruitment outcomes when humans make final decisions. The data combines: Job descriptions Carefully designed candidate profiles (constructed to manipulate demographic cues and job-relevant skills) Algorithmic interventions (e.g., fitness-based, discriminatory, and compensatory rankings) Human shortlisting decisions (participants selecting candidates from ranked lists) More info in README files
algorithmic recruitment, fair outcomes, algoritmic fairness, position bias, fair ranking
algorithmic recruitment, fair outcomes, algoritmic fairness, position bias, fair ranking
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!