AI Selection Ethics

AI selection ethics (Concern 11)

SIOP members are bound by the APA Ethical Principles of Psychologists and Code of Conduct. A review of Section 9 (Assessment) surfaces several ethical concerns for newer forms of assessment. The central message: reliability, validity, and fairness are not separate from ethics — they are intertwined with it. It is the professional responsibility of I-O psychologists to require information about reliability, validity, and fairness when deciding whether a selection system — technology-enhanced or otherwise — can be used to make inferences about job performance.

The governing standards (APA Ethics Code, Section 9)

• 9.01 Bases for Assessments. Psychologists base the opinions in their recommendations, reports, and evaluative statements on information and techniques sufficient to substantiate their findings. → An opinion that an AI tool selects good employees must rest on sufficient evidence, not vendor assertion.
• 9.02 Use of Assessments. Psychologists use instruments whose validity and reliability have been established for the population tested. When validity/reliability have not been established, they describe the strengths and limitations of results and interpretation. → If an AI tool lacks established reliability/validity for your applicant population, that must be disclosed and its limitations described — not glossed.
• 9.03 Informed Consent. Psychologists obtain informed consent for assessments (per Standard 3.10), except when (1) testing is mandated by law or governmental regulation; (2) consent is implied because testing is a routine educational, institutional, or organizational activity (e.g., when participants voluntarily apply for a job); or (3) the purpose is to evaluate decisional capacity. Informed consent includes an explanation of the nature and purpose of the assessment, fees, third-party involvement, limits of confidentiality, and a sufficient opportunity to ask questions and receive answers.

Where AI strains these standards

• Implied consent may not reach incidental data. Implied consent covers routine testing when someone applies for a job — but it is not clear that implied consent extends to data the candidate may not be aware is being obtained (scraped social media, facial/voice analysis). This links directly to ai-candidate-data-control.
• Different consent standards for builders vs. users. The informed-consent standards differ for researchers developing selection tools (Ethics Code 8.05) versus those employing them (Guzzo et al., 2015; Dekas & McCune, 2015). Know which role you're in.
• Sufficient evidence to substantiate findings. If an ML algorithm infers that applicants will have a higher likelihood of good performance, what is the quality and strength of the evidence behind that inference (9.01)? Establishing reliability and validity is an ethical, not merely technical, obligation, because the recommendation made from the test score must be supported.

The integrating point

I-O psychologists must determine whether ethical standards are being met or can be met when working with AI tools. That means they must:

• establish (or require evidence of) validity and reliability of the instruments used for selection (9.02), and
• have the evidence to support the recommendation made from the test score (9.01), and
• ensure appropriate consent (9.03), recognizing the gaps around uncontrolled/unaware data.

In short, the ethical duty operationalizes the scientific concerns: you cannot ethically deploy a tool whose reliability, validity, and fairness you cannot vouch for.

Questions to ask

• Is there sufficient evidence to substantiate the findings/recommendations the tool produces (9.01)?
• Have validity and reliability been established for the population tested — and if not, are strengths/limitations clearly described (9.02)?
• Is informed consent properly obtained or appropriately implied — and does any implied consent actually cover data the candidate is unaware of (9.03)?
• Are you acting as a developer (8.05) or a user of the tool, and have you applied the right consent standard?

Pitfalls

• Relying on vendor claims rather than evidence "sufficient to substantiate findings" (9.01).
• Using a tool without reliability/validity established for your applicant population, and not disclosing the limitation (9.02).
• Stretching "implied consent" to cover scraped or incidental data the candidate never knew about (9.03).
• Confusing the consent obligations of tool developers with those of employers/users.
• Treating ethics as separate from psychometrics rather than as their enforcement.

Checklist

• Evidence is sufficient to substantiate the tool's recommendations (9.01)
• Reliability/validity established for the tested population, or limitations described (9.02)
• Informed consent obtained or properly implied; coverage of unaware/uncontrolled data examined (9.03)
• Developer (8.05) vs. user consent role identified and correct standard applied
• Reliability/validity/fairness evidence required as an ethical precondition for deployment

See also

ai-candidate-data-control (consent for uncontrolled data) · ai-validity-evidence · ai-reliability · ai-selection-legal-landscape (APA Code as professional, not legal) · ai-fairness-lenses (legal/ethical/moral lens) · ai-audit-meta-components (respect / ethical-standards conformance)

Source: Tippins, Oswald & McPhail (2021), Concern: "Ethics."