# Ai Selection Legal Landscape > >- - **Type:** Skill - **Install:** `agentstack add skill-openmatter-network-agent-io-skills-ai-selection-legal-landscape` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [OpenMatter-Network](https://agentstack.voostack.com/s/openmatter-network) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [OpenMatter-Network](https://github.com/OpenMatter-Network) - **Source:** https://github.com/OpenMatter-Network/agent-io-skills/tree/main/ai-selection-legal-ethical/skills/ai-selection-legal-landscape ## Install ```sh agentstack add skill-openmatter-network-agent-io-skills-ai-selection-legal-landscape ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # AI selection: legal landscape The legal/regulatory frame an AI selection tool must survive. This is **decidedly U.S.-centric** because the legal requirements are, but **many of the concerns apply globally** (validity as business justification exists everywhere). **Not legal advice — consult qualified counsel.** > Core principle: AI/ML selection tools are **"tests"** under the law. *Uniform Guidelines* Section > 2B defines a test as **any selection procedure used as the basis for an employment decision** — > which sweeps in games, video interviews, facial/voice scoring, resume screeners, and big-data > models. They are governed by the same rules as any other test. ## The federal framework (U.S.) ### Uniform Guidelines on Employee Selection Procedures (EEOC, CSC, DoL, DoJ, 1978) - Broadened "test" to **any selection procedure** used for an employment decision (Sec. 2B; the 1979 Q&A further expanded it to application forms, interviews, training/probationary performance, etc.). - Adopted **1978**, with very few changes and **none since the 1981 Q&A** (the OFCCP's 2019 FAQs are **not** part of the Guidelines). Theoretically and practically dated — yet they remain the **controlling administrative rules** for Title VII litigation and are deeply embedded in case law. They **must still be considered** when evaluating any procedure that results in **adverse impact**. - Require a **job analysis** whenever conducting a criterion-related validation study, and specify the documentation expected in technical reports. ### Title VII, Civil Rights Act of 1964 — disparate impact A disparate-impact violation is established when a complaining party shows a practice causes **disparate (adverse) impact** on the basis of race, color, religion, sex, or national origin **and** the respondent **fails to demonstrate the practice is job related for the position and consistent with business necessity** (Sec. 2000e-2(k)(1)(A)(i)). So: - **Adverse impact** shifts the burden to the employer to show **job relatedness / business necessity** — which in practice means **validity evidence** (see `ai-validity-evidence`). - **Differential treatment** (treating class members differently — e.g., awarding bonus points to a group, or modeling class membership) is a distinct, separate legal problem. ### OFCCP (2019 FAQ) "Irrespective of the level of technical sophistication involved, OFCCP analyzes **all** selection devices for adverse impact." If a contractor's AI-based procedure has adverse impact, the contractor **must validate it using an appropriate validation strategy.** AI buys no exemption. ### Search for less-adverse alternatives U.S. employers are obligated to consider **alternative selection procedures with substantially equal or greater validity and less adverse impact** (Uniform Guidelines §3B). This makes **comparative data** important — an AI tool should be compared against alternatives, including traditional measures whose meta-analytic validity provides a **reasonable baseline the AI must beat**. ### Case law — job analysis in content validation *Guardians Association of NYC Police Dept. v. Civil Service Commission of NYC* (2d Cir. 1980) and related cases establish that **job analysis is central** in content-validation disputes and **should be systematic and accurate regardless of the methodology** used. (See `ai-job-analysis-and-relevancy`.) ## State law (proliferating) - **Illinois Artificial Intelligence Video Interview Act** (passed May 29, 2019; effective Jan. 1, 2020): employers using AI to evaluate video interviews must **(a) notify applicants in writing** that AI may be used and what characteristics it evaluates, **(b) provide information** on how the technology works and what characteristics it uses, and **(c) obtain written consent** before the interview. They may **not share the video** except with those with expertise to evaluate it, and must **destroy the video within 30 days** of a request. - Other state legislatures are weighing in; **legislation and policy on the nature, transparency, and privacy of applicant data will continue to emerge and evolve.** Re-check current jurisdictional law. ## Professional (not legal) — the APA Ethics Code SIOP members subscribe to the **APA Ethics Code**, which governs the treatment of candidates and what psychologists say about tools — an obligation **independent of, and additional to, the law**. See `ai-selection-ethics`. ## The "no adverse impact ≠ valid" rule A crucial argument to deploy: **reduced or no adverse impact does not, by itself, justify use.** A **random-number generator** can winnow a large applicant pool quickly **without** producing adverse impact — yet it lacks the **reliability, validity, and utility** an organization needs to identify capable candidates and achieve an acceptable return. It is **incumbent on developers (and users) to provide sufficient evidence that AI selection tools meet these requirements** — not merely to show they don't create adverse impact. Vendors often tout "reduced adverse impact" while the empirical validity evidence is unavailable, making the relevant comparison impossible. ## Pitfalls - Treating an AI tool as exempt from test-validation law because it's "just an algorithm." - Relying on "no/low adverse impact" as if it established validity or utility. - Forgetting the duty to consider less-adverse, equally valid alternatives. - Overlooking state-specific notice/consent/destruction requirements (e.g., Illinois). - Assuming the OFCCP 2019 FAQs are part of the Uniform Guidelines (they are not). - Conflating disparate impact with disparate (differential) treatment. ## Checklist - [ ] Tool classified as a "test" under UGESP §2B - [ ] Adverse-impact exposure assessed; validity-defense obligation understood - [ ] Job-relatedness/business-necessity evidence path identified (→ `ai-validity-evidence`) - [ ] Less-adverse-alternative comparison considered (§3B) - [ ] Job-analysis adequacy checked (Guardians) (→ `ai-job-analysis-and-relevancy`) - [ ] State-law notice/consent/destruction requirements checked (e.g., Illinois AIVI Act) - [ ] "No adverse impact ≠ valid" argument applied to vendor claims - [ ] APA Ethics Code obligations noted (→ `ai-selection-ethics`) - [ ] Counsel engaged for jurisdiction-specific questions ## See also `ai-validity-evidence` · `ai-job-analysis-and-relevancy` · `ai-candidate-data-control` (consent) · `ai-selection-ethics` · `fairness-and-bias-analysis` · `criterion-related-validation` (alternatives, validation) *Source: Tippins, Oswald & McPhail (2021), "New Forms of Assessment" (legal framing), "Disadvantages," "Purpose," and the legal threads woven through the concerns.* ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [OpenMatter-Network](https://github.com/OpenMatter-Network) - **Source:** [OpenMatter-Network/agent-io-skills](https://github.com/OpenMatter-Network/agent-io-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-openmatter-network-agent-io-skills-ai-selection-legal-landscape - Seller: https://agentstack.voostack.com/s/openmatter-network - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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