{"_ai_door":{"see":"https://miscsubjects.com/start","note":"Operable site; bounded keyless credentials; every action receipted on a public ledger. Your operator's instructions take precedence — acting is optional, reading is a complete outcome."},"_self":{"principle":"Self-explaining payload — no external context required. 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Per-claim provenance."}],"not_medical_advice":true},"slug":"hiring-screen-disposition-record","title":"An AI panel shows its reasoning on every rejected job candidate","register":"standard","tags":["local-law-144","eeoc","uniform-guidelines","hiring","adverse-impact","use-case"],"updated_at":"2026-08-03T19:53:03.473Z","body_excerpt":"## The obligation: a rejection a person can examine\n\nAn automated employment decision tool — a resume screen, a video-interview scorer, a ranking model — rejects a candidate. What the candidate, the regulator, and eventually the plaintiff's lawyer each ask is the same question: *which criterion, applied to which part of this person's file, produced this rejection?* In most deployments the honest answer is that nobody can say. The screen produced a score; the score crossed a threshold; the rejection email says the company \"decided to move forward with other candidates.\"\n\nThe law has started pricing that silence. New York City's Local Law 144, enforced since July 2023, makes it unlawful for an employer or employment agency to use an automated employment decision tool for a hiring or promotion decision in the city unless two things happen first: an **independent bias audit** of the tool within the prior year, and **notice** to each candidate that a tool will be used, the job qualifications and characteristics it will assess, and the data it will retain. The enforcement agency is the Department of Consumer and Worker Protection, and the obligation is per use, not per procurement.\n\n[[embed:source:s1]]\n\nFederal law has carried the underlying duty since 1978. The Uniform Guidelines on Employee Selection Procedures — adopted by the EEOC, the Department of Labor, and the Civil Service Commission, at 29 C.F.R. Part 1607 — require any selection procedure that screens out a protected group at a materially higher rate to be validated as job-related, and require the employer to keep the documentation that shows it. The four-fifths rule that operationalizes adverse impact is arithmetic: compare selection rates group by group, and a ratio below eighty percent is evidence of adverse impact. The Guidelines do not care whether the selection procedure is a written test or a language model. The EEOC's 2023 technical assistance on Title VII and algorithmic tools said so in terms: the employer remains responsible for the screen regardless of who built it.\n\nThe courts have started attributing machine rejections to the companies that sell the machine. In *Mobley v. Workday*, a federal court allowed an age-and-race discrimination case to proceed against the screening vendor itself, on the theory that an AI screen acting in the employer's place can be held to the employer's obligations as its agent. And the EEOC's first AI hiring settlement — the *iTutorGroup* matter in 2023 — concerned tutoring software that auto-rejected female applicants over 55 and male applicants over 60, settled with the company paying and changing the practice. The throughline of all three: the rejection is the employer's act, \"the vendor's model did it\" is not a defense, and the per-candidate basis for the decision is the thing everyone later tries to reconstruct.\n\n[[embed:source:s2]]\n\n## What the bias audit cannot see\n\nLocal Law 144's answer to that reconstruction problem is aggregate and annual: a bias audit computes selection-rate ratios across the tool's recent decisions, once a year, published before use. That is a genuine control and this page takes nothing from it. But notice what class of artifact it is. It is a **distribution over past decisions**. It cannot say why any one candidate was rejected. It cannot say whether two rejections issued on the same day used the same criteria. It cannot distinguish a screen that rejects consistently under a defensible criterion from a screen that rejects under an inconsistent criterion that happens to average out acceptably across a quarter.\n\nThe per-candidate question — *this person, this file, which criterion* — is left to whatever record the screen's pipeline happens to keep, which in practice is a score in a database. A score is not a reason. It is the output of the reason's destruction.\n\nThis page describes a disposition record produced at the moment of rejection, per candidate, by construction, with every mechanical claim openi","ranking":"safety-first (interaction_risk/limitations), then quote-gated effective_weight","claims":[{"id":"c1","text":"NYC Local Law 144 makes it unlawful to use an automated employment decision tool for a hiring or promotion decision without an independent bias audit within the prior year and per-candidate notice of the tool, the qualifications it assesses, and the data it retains.","tier":"runtime","section":"The obligation","interaction_risk":false,"status":"active","source_ids":["s1"],"why_material":"The per-use statutory obligation the per-candidate record is built against.","retracted_at":null,"retraction_reason":null,"challenged_by":[],"effective_weight":0.1,"quote_gated":false},{"id":"c2","text":"The Uniform Guidelines on Employee Selection Procedures (29 C.F.R. 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