Explainable AI
What is explainable AI in hiring?
Explainable AI means an AI system's outputs can be understood and traced: you can see how a result was produced, not just what it is. In hiring, explainability is what lets recruiters trust, defend, and correct a score.
Why it matters
A score you cannot explain is a score you cannot defend, to a candidate, a manager, or an auditor. Explainability builds trust, catches mistakes, and supports laws that require transparency in automated employment tools.
Example
A candidate asks why their resume scored lower than expected. With an explainable system, the recruiter can point to specific, job-related factors: a missing required skill, less relevant experience, each with a visible weight. With a black box, the only honest answer would be “the model decided,” which satisfies no one.
How uRecruits approaches it
Scoring uses transparent, published logic. Employer-side matching is deterministic (criteria and weights you can inspect), and the candidate-facing ATS Resume Score publishes its six weighted categories so applicants see where each point came from. Every score has a defined source, and AI assists while people decide. Explainable scoring is itself one of the four commitments uRecruits' Responsible AI Program (RAP) is formalizing, alongside human authority over decisions, external bias auditing, and a published decision-log approach. RAP is in active development, not a finished certification.
