Recruiting AI evidence records for audit-ready hiring workflows
For AI systems used in recruitment, screening, ranking, shortlisting, or hiring decisions, compliance teams need a defensible record of what the system did and why a human reviewer trusted, rejected, or overrode the result.
Built for compliance, HR risk, legal, and engineering teams that operate AI-assisted hiring systems and need traceable, tamper-evident decision evidence.
Sector risk context
What compliance teams need to prove
Recruiting AI decisions can affect access to employment and career opportunity. A reviewable record should show the role, candidate reference, scoring or ranking context, model or ruleset version, reason codes, human oversight status, and whether the record was modified after creation.
Evidence model
Evidence fields to capture at decision time
Candidate reference
Use a pseudonymous candidate or application ID rather than storing unnecessary personal data in the evidence record.
Role and workflow
Capture the job requisition, screening stage, workflow owner, and whether the decision was ranking, rejection, shortlist, or escalation.
Model and policy version
Record the AI system, model version, prompt or rubric version, and any hiring policy used at decision time.
Reason codes
Store structured reason codes and a concise rationale summary suitable for later review.
Human oversight
Track whether human review was required, performed, overridden, or bypassed.
Verification metadata
Include hash, signature, key ID, timestamp, and verification URL so the record is independently checkable.
Audit questions
Questions this evidence trail should answer
- Which system screened or ranked the candidate?
- Which role, stage, model, and policy context applied?
- Was the candidate rejected, ranked, shortlisted, or escalated?
- Was human review available and actually used?
- Can the record be verified without administrator access?
Workflow
From AI output to reviewable evidence
- 1
Capture the decision
Record the decision event, subject, system, model version, inputs or references, and reason codes at the moment the AI system acts.
- 2
Sign the payload
Canonicalize the record, compute a SHA-256 hash, sign with Ed25519, and preserve the key ID for later verification.
- 3
Link evidence
Reference datasets, model artifacts, prompts, policy versions, human review actions, and system configuration where they affect the outcome.
- 4
Export for review
Generate JSON or PDF evidence bundles that compliance, legal, procurement, or regulators can inspect without production access.
Guardrails
Evidence support is not a compliance guarantee
Evidence does not equal legal conclusion
A signed record proves integrity and provenance of the evidence record. It does not prove that the underlying decision was fair, lawful, accurate, or sufficient on its own.
Minimize sensitive data
Use pseudonymous identifiers, references, and redaction rules so the evidence trail supports review without overcollecting personal data.
Start with proof
Generate one signed decision record and verify it yourself.
The anonymous demo shows the evidence model before any integration: payload, hash, signature, key ID, verification result, and exportable record.
Related evidence surfaces