Framework · NYC Local Law 144
Local Law 144 evidence for the tools that rank candidates.
New York City has enforced Local Law 144 on automated employment decision tools since 5 July 2023, with an annual independent bias audit, a published summary, and candidate notice. Meilynx evidences which tools actually ran and carries the audit as attested evidence.
Audit, publish, notify.
Local Law 144 applies to automated employment decision tools used to substantially assist or replace discretionary decisions on hiring or promotion for jobs in the city, enforced by the Department of Consumer and Worker Protection since 5 July 2023.
- An independent bias audit within one year before an automated employment decision tool is used (§20-871(a)).
- A public summary of the audit results (§20-871(a)).
- Notice to candidates at least ten business days before the tool is used, with the data-type, source, and retention disclosure (§20-871(b)).
- Adverse-impact record-keeping under the EEOC Uniform Guidelines, whose four-fifths analysis sits beside the audit (29 CFR §1607.4).
Statutory duties, to runtime evidence.
A specific Meilynx control for each obligation the proxy can substantiate, and the artifact it produces. The bias audit, the published summary, the candidate notice, and the adverse-impact analysis stay attested in your program.
NYC Local Law 144 → Meilynx controls (AI-traffic slice)
| Requirement | How Meilynx maps | Examination artifact |
|---|---|---|
Know which automated employment decision tools are in use NYC LL 144 · §20-871(a) | The inventory of hiring-AI models and providers is derived from live traffic and enforced by runtime model allow-listing. The set of tools that must carry a current bias audit and be named in candidate notices starts from what actually runs. | Automated employment decision tool inventory, generated from traffic |
Know what candidate data is collected and sent to the tool NYC LL 144 · §20-871(b) | Personal-data detection over hiring-AI traffic evidences the types of candidate data actually flowing to each tool: the fact base behind the data-type, source, and retention disclosure. | Candidate personal-data findings by tool |
Record when each tool ran, on what data, and what it produced NYC LL 144 · §20-871(a) · 29 CFR §1607.4 | Each hiring-AI request, response, and governance decision is captured inline at the proxy and sealed into a tamper-evident, hash-chained record. When a complaint asks which tool ran on which candidate, that record answers. | Hash-chained decision audit trail for the period |
Evidence the notice to candidates ten business days before use NYC LL 144 · §20-871(b) | A notice template on the careers page does not show that a specific candidate saw it ten business days before the tool ran. The package carries the notice text, delivery records, and dates as attested evidence. | Notice evidence and the tools each notice must cover, attested |
Evidence the independent bias audit, its publication, and the adverse-impact analysis NYC LL 144 · §20-871(a) · 29 CFR §1607.4 | The bias audit is performed by an independent auditor by statute. The package carries the auditor, audit date, distribution date, published-results URL, and your four-fifths analysis as attested evidence. | Bias-audit log and notice evidence, attested |
Know which automated employment decision tools are in use
NYC LL 144 · §20-871(a)
Maps to · The inventory of hiring-AI models and providers is derived from live traffic and enforced by runtime model allow-listing. The set of tools that must carry a current bias audit and be named in candidate notices starts from what actually runs.
Examination artifact · Automated employment decision tool inventory, generated from traffic
Know what candidate data is collected and sent to the tool
NYC LL 144 · §20-871(b)
Maps to · Personal-data detection over hiring-AI traffic evidences the types of candidate data actually flowing to each tool: the fact base behind the data-type, source, and retention disclosure.
Examination artifact · Candidate personal-data findings by tool
Record when each tool ran, on what data, and what it produced
NYC LL 144 · §20-871(a) · 29 CFR §1607.4
Maps to · Each hiring-AI request, response, and governance decision is captured inline at the proxy and sealed into a tamper-evident, hash-chained record. When a complaint asks which tool ran on which candidate, that record answers.
Examination artifact · Hash-chained decision audit trail for the period
Evidence the notice to candidates ten business days before use
NYC LL 144 · §20-871(b)
Maps to · A notice template on the careers page does not show that a specific candidate saw it ten business days before the tool ran. The package carries the notice text, delivery records, and dates as attested evidence.
Examination artifact · Notice evidence and the tools each notice must cover, attested
Evidence the independent bias audit, its publication, and the adverse-impact analysis
NYC LL 144 · §20-871(a) · 29 CFR §1607.4
Maps to · The bias audit is performed by an independent auditor by statute. The package carries the auditor, audit date, distribution date, published-results URL, and your four-fifths analysis as attested evidence.
Examination artifact · Bias-audit log and notice evidence, attested
What you show the Department of Consumer and Worker Protection.
The audit trail renders into an evidence package scoped to the AI-traffic slice. Each control is classified as proxy-verified runtime evidence or attested in your program, and the bias-audit log records whether the audit is current.
In the package
- Automated employment decision tool inventory, auto-populated from traffic.
- Bias-audit log: auditor, audit date, distribution date, published URL (attested).
- Notice evidence and the tools each notice must cover.
- Tamper-evident decision audit trail across the reporting period.
- Obligation timeline: NYC and Illinois in force, Colorado readiness, EU AI Act pointer.
Local Law 144 and the proxy.
Which tools does Local Law 144 reach?
Automated employment decision tools used to substantially assist or replace discretionary decisions on hiring or promotion for jobs in New York City. Applicant tracking systems ship ranking features that meet that definition, and if nobody decided whether the feature is an automated employment decision tool, the audit clock never started. The inventory derived from live traffic is where that scoping call begins; the call itself is yours.
How does the bias audit appear in the package?
The package carries the auditor, audit date, distribution date, published-results URL, and your four-fifths analysis as attested evidence. The bias-audit log records whether the audit is current.
Which candidate data does the evidence show?
Personal-data detection over hiring-AI traffic shows the types of candidate data flowing to each tool. That is the fact base behind the data-type, source, and retention disclosure in the candidate notice.
Build the evidence.
See exactly what an examiner receives
Request a sample examination package: model inventory, control coverage, a governance policy snapshot, and a SHA-256 integrity hash.