Illustrative walkthrough using seeded data. Product capabilities are marked Built or Planned.
NOTARY AI
Screen 04[BUILT]

Candidate HS-2211 rejected on age-proxy feature

Hiring · EEOC / age discrimination (ADEA) — one entry in Company XYZ's versioned library of : preserved decision conditions paired with an Authorized Expected Behavior, reused on every future candidate version.
Company XYZ decision accountability · LoanCore Underwriting 3.2.0Hiring · Candidate HS-2211Representative lending boundary case
  1. ACCOUNT13
  2. VERIFY47
  3. DEFEND89
  1. Connect
  2. Landscape
  3. Readiness
  4. 4Scenarios
  5. 5Authorize
  6. 6Verify
  7. 7Release
  8. 8Package
  9. 9Hand-off
VERIFYScreen 04
Question
Which decision conditions must future versions continue to handle correctly?
Answer
Company XYZ has preserved representative, boundary, disputed, and previously failed decisions as reusable Assurance Scenarios.
Output
A versioned library of decision conditions with authorized expected outcomes.
Fault attribution · Biased feature

A proxy feature (age via graduation year) dominated the screening score.

Decision chain

[BUILT]captured evidence
INPUT
Resume features
grad_year=1994, 22y experience, 4 relevant skills
TOOL
Feature extractor
Includes years_since_graduation
MODEL
Failing or uncertain node
Screening model v0.9
Coefficient on grad-year proxy: -0.47
RULE
Auto-reject threshold
score<0.4 → reject
DECISION
Rejection email sent
Auto-response within 4 min
Why this case is useful for release assurance
source_decision
Candidate HS-2211, staff engineer role
trigger
54 screening rejections weighted by an age-proxy feature
failing_node
Screening model v0.9
evidence_level
E4
replayability
Replay-ready from the sealed cassette
missing_evidence
Feature attributions are captured for only 31% of screening decisions.
applicable_policy
Screening Fairness Standard 1.3 (2026-05)
expected_behavior
Authorized by HR Compliance Counsel — Sasha Kim
suggested_preservation
Preserve decision-time policy version and human-review evidence on the next occurrence
suggested_scenario
Add to the Hiring Release Scenario set as a historical failure preserved as a scenario
Candidate provenance
evaluator
company-xyz-evaluator v0.6.2
frozen_input_set
hiring-sweep-2026-07
evaluated_at
2026-07-22T04:15:00Z
source_evidence
sha256:c14e…f0b8
relationship_confidence
Confirmed — decision and adverse-action record share a source identifier
known_limitations
Source not fully connected — coverage partial; advisory only.
Captured decision
Candidate HS-2211, staff engineer role
Outcome
REJECTED at screening (score 0.31)
reason: Feature 'years_since_graduation' dominated score
Authorized Expected Behavior
Advance to recruiter review; age-proxy features excluded.
Evidence
E4sufficient for verification
cassette · sha256:c14e…f0b8
Next action

Advisory only. An must define or approve the expected behavior before a candidate version can be verified against it.

→ Authorize expected behavior
Screen anatomy

What this screen proves

For any single decision case, Notary AI can show which nodes ran, which node is failing or uncertain, what was captured, what evidence is missing, and what the record is at.

Why it matters

This is how a case earns a place in the release-assurance suite. A useful case has preserved evidence, a known applicable policy, and a clear expected behavior worth testing every future candidate version against.

Built or Planned

Planned capability for candidate generation; the decision-chain view and evidence grading are built. Candidates shown here are a seeded illustration.

Important boundary

The candidate is advisory. Notary AI does not determine what the acceptable outcome should be — an supplies the Authorized Expected Behavior on Screen 05, and the re-checks eligibility server-side.

Related requirements

Next

A scenario preserves the conditions, but it cannot decide what outcome is acceptable. An authorized Company XYZ reviewer must establish that.

Authorized Behavior