Fairness Metrics in Recruitment Screening: Incompatibility Results and What Practitioners Choose Instead
DOI:
https://doi.org/10.5281/zenodo.22660664Keywords:
Fairness Metrics in Recruitment Screening; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Fairness Metrics in Recruitment Screening is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for fairness metrics in recruitment
screening, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has converged
and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We conclude
that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.
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