| name | ai-extending-professional-standards |
| description | 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". |
| version | 1.0.0 |
| author | OpenMatter-Network |
| license | MIT |
| category | research |
| tags | ["Community","io-psychology","ai-assessment","legal-ethical"] |
| permissions | [] |
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
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.
