| name | ai-selection-ethics |
| description | Use when evaluating the professional-ethics obligations around an AI/ML personnel selection tool under the APA Ethics Code — Concern 11 of Tippins, Oswald & McPhail (2021). Covers Ethics Code Section 9 (9.01 Bases for Assessments, 9.02 Use of Assessments, 9.03 Informed Consent), the difference in consent standards for researchers (8.05) vs. those employing tools, and how reliability, validity, and fairness are intertwined with ethical duties. Triggers: "APA ethics AI hiring", "is it ethical to use this assessment", "informed consent assessment", "ethics code section 9", "psychologist responsibility AI selection", "implied consent job applicant". |
| version | 1.0.0 |
| author | OpenMatter-Network |
| license | MIT |
| category | research |
| tags | ["Community","io-psychology","ai-assessment","legal-ethical"] |
| permissions | [] |
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
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."
