From the hash-bound evidence releases
Recorded claims
Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.
The review concerns algorithmic-bias mitigation in machine-learning systems used for personnel assessment and selection, including résumé screening and automatically scored interviews.
Scope: search-result snippet and bibliographic metadata; publisher abstract not inspected
Structured relationships
In-corpus citations
Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.
Cites (3)
- Using machine learning to translate applicant work history into predictors of performance and turnover.
- Adverse impact reduction and job performance optimization via pareto-optimal weighting: A shrinkage formula and regularization technique using machine learning.
- Turning words into numbers: Assessing work attitudes using natural language processing.
Cited by (0)
No in-corpus incoming citation is available in the current enrichment coverage.
Boundaries
Limitations & independence
Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.
Recorded limitations
- Crossref supplied bibliographic metadata but no abstract.
- The personnel-selection context is based on a retained Google Scholar result snippet; publisher abstract and full text were not inspected.
- Crossref supplied only year and month, so published_date is null rather than an inferred day.
Source independence
corroborated:google-scholar-result