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AI USE CASE
CV screening & candidate shortlisting
Rank inbound CVs against role criteria with bias-aware scoring.
What it is
An NLP model parses CVs and scores candidates against the role's must-have and nice-to-have criteria, with explicit bias-mitigation controls (anonymisation, de-biased features, audit trail). Recruiters spend their time on shortlists, not stacks.
Data you need
Job descriptions with structured criteria, ATS access.
Required systems
- project management
Why it works
- Bias audit before deployment and quarterly thereafter
- Always require human review of top 10 and bottom 10
How this goes wrong
- Model amplifies bias from historical hiring patterns
- Recruiters trust scores blindly and stop reviewing
When NOT to do this
Don't auto-reject candidates based on model score alone, EU AI Act treats this as high risk.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.