# AI Recruiting Primary-Source Ledger > Review 18 sources by type, jurisdiction, supported use, and evidence limit. This ledger separates binding rules, government guidance, professional practice, voluntary frameworks, technical standards, and first-party research. - Canonical HTML: https://openjobs.genedai.me/sources - Last substantive review: 2026-08-07 - Scope: Source interpretation; not legal advice or independent product validation. - Download: [Evidence register CSV](https://openjobs.genedai.me/downloads/ai-recruiting-evidence-register.csv) ## Public rules, guidance, frameworks, and practice ### NIST: Artificial Intelligence Risk Management Framework 1.0 - URL: https://www.nist.gov/itl/ai-risk-management-framework - Type and scope: Voluntary framework; global reference published in the United States. - Supports: Lifecycle work across Govern, Map, Measure, and Manage. - Limit: Voluntary use does not establish legal compliance, product quality, or fitness for a hiring process. ### NIST: AI Risk Management Framework Playbook - URL: https://www.nist.gov/itl/ai-risk-management-framework/nist-ai-rmf-playbook - Type and scope: Voluntary implementation resource. - Supports: Suggested actions for applying the AI RMF functions. - Limit: The actions are optional and use-case agnostic; they do not form a certification checklist. ### EEOC: Employment Tests and Selection Procedures - URL: https://www.eeoc.gov/laws/guidance/employment-tests-and-selection-procedures - Type and scope: U.S. federal technical assistance. - Supports: Review of job-related selection procedures, discriminatory impact, validation, and employer responsibility. - Limit: The page does not determine whether a particular tool, employer, or use is lawful. ### eCFR: 29 CFR Part 1607, Uniform Guidelines on Employee Selection Procedures - URL: https://www.ecfr.gov/current/title-29/subtitle-B/chapter-XIV/part-1607 - Type and scope: U.S. federal rule text. - Supports: Documentation, impact, and validity requirements for covered selection procedures. - Limit: The text alone does not resolve coverage, statistical sufficiency, defenses, or obligations in a particular matter. ### ADA.gov: Algorithms, Artificial Intelligence, and Disability Discrimination in Hiring - URL: https://www.ada.gov/resources/ai-guidance/ - Type and scope: U.S. government guidance on disability and hiring. - Supports: Review of disability-related screening risk, accommodations, accessibility, and notice. - Limit: The informal guidance cannot decide whether a particular process complies with the ADA. ### OPM: Job Analysis - URL: https://www.opm.gov/policy-data-oversight/assessment-and-selection/job-analysis/ - Type and scope: U.S. federal personnel practice. - Supports: Definition of job tasks, competencies, context, and assessment content. - Limit: The practice does not validate a private-sector role brief or automated assessment. ### OPM: Structured Interviews - URL: https://www.opm.gov/policy-data-oversight/assessment-and-selection/structured-interviews/ - Type and scope: U.S. federal personnel practice. - Supports: Predetermined job-related questions, consistent administration, and shared rating standards. - Limit: Structure does not guarantee validity, fairness, accessibility, or a correct decision. ### UK DSIT: Responsible AI in Recruitment - URL: https://www.gov.uk/government/publications/responsible-ai-in-recruitment-guide/responsible-ai-in-recruitment - Type and scope: UK government guidance. - Supports: Procurement and deployment questions covering purpose, governance, accessibility, assurance, testing, pilots, transparency, and monitoring. - Limit: The guide does not provide legal assurance or universal deployment instructions. ### ICO: AI Tools Used in Recruitment, Audit Outcomes - URL: https://ico.org.uk/action-weve-taken/audits-and-overview-reports/2024/11/ai-tools-used-in-recruitment/ - Type and scope: UK data-protection audit outcomes. - Supports: Observed privacy and information-rights issues in recruitment tools and reported remediation themes. - Limit: Audits of selected providers do not establish prevalence, legal status, or performance of another product. ### W3C: Web Content Accessibility Guidelines 2.2 - URL: https://www.w3.org/TR/WCAG22/ - Type and scope: Technical web standard; legal adoption varies. - Supports: Testable web-content accessibility criteria. - Limit: Web-content conformance does not establish that the full hiring process is accessible or lawful. ### NYC DCWP: Automated Employment Decision Tools - URL: https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page - Type and scope: New York City rule materials. - Supports: Official access to Local Law 144 materials on covered use, bias audits, public summaries, and notice. - Limit: The overview does not determine coverage or compliance for a particular system. ### European Commission: Navigating the AI Act - URL: https://digital-strategy.ec.europa.eu/en/faqs/navigating-ai-act - Type and scope: European Union government guidance. - Supports: Current explanations of scope, risk classification, employment uses, obligations, and implementation timing. - Limit: Timing can change, and classification depends on intended purpose and deployment facts. ## First-party Metix research OpenJobs AI is now [Metix AI](https://metix.ai/about). The sources below explain what Metix says it built, measured, or learned. They are not independent validation of a buyer's deployment. ### Mira: The First End-to-End AI Recruiter - URL: https://metix.ai/research/mira-end-to-end-ai-recruiter - Supports: Metix's description of recruiting-agent boundaries, retrieval, matching, evaluation, and handoffs. - Limit: The report does not independently validate performance, customer outcomes, compliance, or fit for another workflow. ### Agent Evaluation, Done Right - URL: https://metix.ai/research/agent-evaluation-done-right - Supports: A component, trajectory, and outcome model for agent evaluation. - Limit: The method is first-party and requires reproduction in the buyer's environment. ### Performance Drift in Agent Systems - URL: https://metix.ai/research/agent-performance-drift - Supports: A first-party account of longitudinal evaluation and change-aware monitoring. - Limit: The article does not establish the presence, absence, or rate of drift in a specific deployment. ### Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment - URL: https://metix.ai/research/mira-embeddings-v1 - Supports: Reported retrieval and reranking metrics, dataset protocols, and boundary-aware modeling. - Limit: The results are protocol-specific and do not establish live-role quality, fairness, or general superiority. ### When AI Meets Recruiting - URL: https://metix.ai/research/ai-meets-recruiting - Supports: A lifecycle taxonomy of AI applications and open questions across recruiting stages. - Limit: The review does not validate a product, employer decision, or universal division of human and machine work. ### Hiring Outcomes, Not More Software - URL: https://metix.ai/blog/hiring-outcomes-not-software - Supports: A first-party argument for evaluating systems by delivered hiring progress and operating burden. - Limit: The product perspective is not an independent return-on-investment study or evidence of fit for every employer. ## How to use the ledger Attach a source to each claim, define the evidence expected in the buyer's workflow, test it on a real role, and record where the result differs from the demo or documentation. Use the [evaluation methodology](https://openjobs.genedai.me/methodology), [vendor checklist](https://openjobs.genedai.me/vendor-checklist), and [evaluation scorecard](https://openjobs.genedai.me/evaluation-scorecard) to structure the review.