Answer capsule
Bias audits, published summaries, and notices turn vendor and employer process into a reviewable record.
What the source establishes
- Local Law 144 covers certain automated employment decision tools.
- Covered use requires a recent bias audit and public summary.
- Candidate or employee notice requirements apply.
Scope before audit
The first question is whether the configured tool and actual use fall within the definition. A vendor's generic label cannot answer that for the employer.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
An audit is not absolution
A published metric can reveal important disparities but does not prove job relatedness, accessibility, data quality, or nondiscrimination in every decision.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Notice needs an experience
A notice should connect to an understandable explanation, accommodation route, data policy, and human contact—not merely satisfy a posting step.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Maintain deployment evidence
Keep the use dates, version, job groups, audit scope, public posting, notices, changes, and responsible owners in one record.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Decision test
Ask whether the source changes the decision itself, the evidence required, the implementation sequence, or only the language used to describe an existing capability. Record which claims are directly supported, which are provider statements, which require an independent test, and which remain unknown. A source-linked review should make uncertainty easier to see, not bury it inside a blended score.
Questions to take into review
- Which business demand and skills definitions drive the model?
- How are contingent and affected worker populations represented?
- Does the tool materially influence who advances?
- What validated job criteria support the output?
- Who owns the skills taxonomy?
- Can employees inspect and correct their profile?
- Which learning outcome is being assessed?
- What data is visible to managers or used in employment decisions?
The publication supports research and executive decision preparation. It does not provide legal, financial, accounting, employment, clinical, cybersecurity, investment, procurement, or implementation advice.