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Recovered source transcript
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# Scoring Methodology and Model Card
Model version: v1.0.0
Aligned to NIST AI RMF and ISO/IEC 42001. This document is the scoring methodology and model card
required by the responsible AI rules in CLAUDE.md.
## What this model does
The matching engine scores how well a candidate fits a requirement for cleared workforce qualification
(screenMATCH) and proposal staffing (slateMATCH). It is an automated employment decision tool under
NYC Local Law 144, Title VII, the Colorado and California fair employment laws, and the EU AI Act, with
vendor exposure recognized in Mobley v. Workday. The platform supplies the data and controls for an
independent bias audit; the deploying employer runs that audit. Vendor tooling does not transfer the
employer's liability.
## How the score is computed
The score is a pure deterministic function of fixed weights, computed with exact decimal arithmetic so
the same inputs always produce the same output (the reproducibility the compliance asset requires).
Match Score = Clearance times 0.25 + Skills times 0.25 + Certifications times 0.15
+ Customer Experience times 0.15 + Experience times 0.10 + Location and Availability times 0.10
Bands: Green (Proposal Ready) at 90 and above, Yellow (Potential) from 75 to 89.99, Red (Recruiting
Required) below 75. Every score carries a per component trace, a human readable rationale, and a gap
summary, surfaced as the audit and candidate facing explanation of what was assessed and why.
## What the model never uses
The model takes no demographic input and performs no demographic inference. Sex, race or ethnicity,
veteran status, and disability status live in a physically and privilege segregated eeo schema that the
application and scoring database roles cannot read at all (an attempt fails with a database permission
error). Demographic data flows one way, into adverse impact reporting run by a separate compliance
role, and never into a score. Clearance is a self reported indicator labeled clearance intelligence,
never verified; the real check is the hiring client FSO through DISS at onboarding, and clearance is
encoded as a qualification hierarchy, never as a proxy for any protected attribute.
## Data used
Clearance level, polygraph, and status indicators; skills; certifications; customer agency experience;
years of experience; work location, remote, relocatable, and availability flags. Candidates receive a
notice and disclosure record of what the tool assesses and the data categories used. Identifiable
screening artifacts are retained for a configurable window (default 90 days after a decision), after
which the candidate PII is anonymized in place while the de identified scored outcome is retained for
the recordkeeping the law requires.
## Governance
A human approves or rejects every slate member; the engine never auto rejects (the decision states are
pending, approved, and rejected, with no automated rejection state). Every model version is recorded in
a registry with its weights and band cuts, and each frozen decision is stamped with the version that
produced it. Each org maintains an AEDT inventory of the tools in use. Adverse impact is reported with
selection and scoring rates and four fifths impact ratios across sex and race or ethnicity, including
intersectional categories and an unknown count, flagging any ratio below 0.80.
## Limitations
Four fifths ratios over a small sample are not meaningful; the report surfaces the count and the unknown
category so a ratio is not mistaken for a finding. Semantic skill matching is an assist on top of exact
matching, not a replacement. Any future ranking layer that uses a language model sits on top of this
deterministic score and never replaces it. The model is a fit estimate, not a hiring decision, and not a
statement about a person's protected characteristics.