# Ai Extending Professional Standards > >- - **Type:** Skill - **Install:** `agentstack add skill-openmatter-network-agent-io-skills-ai-extending-professional-standards` - **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-extending-professional-standards ## Install ```sh agentstack add skill-openmatter-network-agent-io-skills-ai-extending-professional-standards ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Extending professional standards (Call to Action) The article's thesis and orchestration point: AI/technologically enhanced selection should be **held to the same established professional standards** as any other employment test, and **I-O psychologists should lead** in working out how. Use this skill to orient a project — or a professional-policy discussion — to the governing documents and the collaborative path forward. It ties the whole collection together. ## The two guiding documents Two documents guide research and practice in employee selection **regardless of the form of assessment:** - **Principles for the Validation and Use of Personnel Selection Procedures** (SIOP, 2018) — see the `personnel-selection` collection. - **Standards for Educational and Psychological Testing** (AERA, APA, NCME, 2014). Both adopt the same definition of validity — *the degree to which **accumulated evidence and theory** support specific interpretations of test scores for proposed uses* — which is exactly why a technology is never "universally valid" (`ai-selection-tech-data-algorithms`) and why validity/reliability/fairness evidence is required for AI tools. ## Why I-O psychologists are well-equipped — but not sufficient alone I-O psychologists bring deep grounding in the factors critical for employment testing: **psychological constructs** (knowledge, personality, interests, engagement, teamwork, safety, performance, turnover), **theories of testing and assessment** (construct-oriented test development, psychometric modeling, appropriate scoring/interpretation), the **types of evidence** that support inferences (selection decisions, validity), **psychometric properties** (internal consistency, test–retest, alternate-forms reliability), and the **evaluation of subgroup differences** (differential prediction, measurement invariance, adverse impact). SIOP also has a long history of **documenting consensus** in the *Principles*. **But this knowledge must be supplemented** by others in the field: **data scientists and software developers** (acquire/store/analyze data, build and evaluate algorithms), **web designers and IT professionals** (build engaging, effective interfaces), and the **legal profession** (compliance with federal/state/local law and regulatory requirements). I-O psychologists cannot regulate others' practice, but many serve as **experts advising organizations and government and testifying** about assessments — supporting *and* challenging them. ## The Call to Action - **Develop interpretive guidance — don't rewrite the *Principles*.** The recommendation is for **SIOP to develop interpretive guidance that *applies* the *Principles* to technologically enhanced assessments**, guiding developers and users in best practices and addressing the open questions the paper raises. The *Principles* already reflect the established science of selection; the goal is **interpretation and consistency**, not replacement. - **Collaborate across disciplines.** Engage applied statistics, computer science, and other fields to learn about ML applications. Together, **identify the strengths, critique the weaknesses, and understand appropriate vs. inappropriate applications.** Interpretive guidance should help **fill knowledge gaps** among the participating parties. - **Engage proactively** — not only selection specialists, but also those in recruiting, diversity and inclusion, and leadership — because doing so can **improve assessment and promote the future relevance of the profession.** ## The guardrail: no "escape velocity" The overarching responsibility: ensure that **progress does not approach escape velocity from its moorings** in scientific, psychometric, and practical knowledge; understanding of legal guidelines and professional/ethical obligations; and the **many hard lessons learned** in the employment-testing arena. New tools offer real advantages for employers and applicants — **and** we are responsible for keeping them anchored. Now is the time to consider how the *Principles* should be applied to new and evolving forms of assessment to reflect the research literature and best practices. ## How to use this skill - **Orient any AI-selection project** to the *Principles* and *Standards* as the benchmark, then route to the specific concern skills for the evaluation. - **Frame professional/policy discussions** around *applying* (not rewriting) the *Principles* and building interdisciplinary collaboration. - **Audit your team composition**: do you have psychometric, data-science, IT/UX, and legal expertise at the table? - **Apply the escape-velocity test**: is any practice drifting away from scientific, legal, or ethical moorings? ## Pitfalls - Proposing to **rewrite** the *Principles* for AI rather than developing interpretive guidance that **applies** them — the established science of selection still holds. - Treating a technology as "validated" rather than validating the **inferences** about constructs measured in a specific use. - Assembling a team with psychometric expertise but **no data-science, IT/UX, or legal** voices (or vice versa) — the paper stresses no discipline suffices alone. - Inventing ad hoc, tool-specific rules disconnected from the *Principles* and *Standards*. - Letting innovation outrun (reach "escape velocity" from) scientific, legal, and ethical moorings in the name of efficiency. ## Checklist - [ ] Project benchmarked against the *Principles* and *Standards* - [ ] Validity framed as evidence + theory for a specific inference (not a property of the technology) - [ ] Interdisciplinary expertise assembled (psychometrics + data science + IT/UX + legal) - [ ] Stance taken: interpret/apply the *Principles* to the tool, not invent ad hoc rules - [ ] Open questions from the 11 concerns logged for the developer/vendor - [ ] "Escape velocity" check applied to scientific, legal, and ethical moorings ## See also All skills in this collection (this is the orchestration point) · `personnel-selection` (the *Principles* operationalized) · `ai-personnel-assessment` (the audit framework) · `validation-planning` · `technical-validation-report` *Source: Tippins, Oswald & McPhail (2021), "Standards" and "A Call to Action," and the Conclusion.* ## 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-extending-professional-standards - 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. Every listing is security-reviewed. Creators keep 70%.