# AI Extending Professional Standards

> Use when framing the profession-level response to AI selection tools, or orienting a project to the governing standards — the "Call to Action" of Tippins, Oswald & McPhail (2021). Covers the Principles and Standards as the two guiding documents, the argument that SIOP should develop interpretive guidance APPLYING the Principles to technologically enhanced assessments (not rewrite them), the need for interdisciplinary collaboration, and the warning against letting practice reach "escape velocity" from scientific, legal, and ethical moorings. Triggers: "how should the profession respond to AI hiring", "extend the Principles to AI", "interpretive guidance for AI assessments", "what standards govern AI selection", "I-O psychologists role in AI hiring", "call to action AI selection".

- Skill: `openmatter-network/ai-extending-professional-standards` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add openmatter-network/ai-extending-professional-standards`
- Raw SKILL.md: https://api.skillmd.com/api/skills/openmatter-network/ai-extending-professional-standards/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: OpenMatter-Network (https://skillmd.com/u/openmatter-network)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/openmatter-network/ai-extending-professional-standards

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# 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.*