---
name: openmatter-network/ai-selection-ethics
source: https://app.decimal.ai/s/openmatter-network-ai-selection-ethics@1/SKILL.md
source_sha256: 9042355d0837
---

# 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."*