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Get Started Free →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"
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 42% | 0% |
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.
Two documents guide research and practice in employee selection regardless of the form of assessment:
the personnel-selection collection.
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.
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.
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.
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.
inclusion, and leadership — because doing so can improve assessment and promote the future relevance of the profession.
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.
to the specific concern skills for the evaluation.
building interdisciplinary collaboration.
at the table?
moorings?
applies them — the established science of selection still holds.
measured in a specific use.
vice versa) — the paper stresses no discipline suffices alone.
the name of efficiency.
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.
Other measured skills in the registry, with their headline benchmark lift.