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name: openmatter-network/ai-audit-meta-components
source: https://app.decimal.ai/s/openmatter-network-ai-audit-meta-components@1/SKILL.md
source_sha256: eae68ad62942
---

# AI audit meta-components (Components 10–12)

These three components are **meta** because they must be considered **across all the other components**,
not as a separate stage. They ask: in what cultural and ethical context does this system operate, and
is the **evidence** behind every claim methodologically sound?

## Component 10 — Cultural context

**What it is:** the broader cultural setting in which the algorithm is used, and whether affected
communities had a voice in it.

**Questions to ask:** Has the broader **cultural context** been considered? Have **members of the
community participated** in the design of systems that will affect them?

**Apply it (focal example):** Do **power differentials** exist between designers, employers, and job
candidates? Have **cultural assumptions** been made? Will development decisions made **in one culture
be applied to another** — and if so, how has the development process been **adjusted to prevent
cross-cultural application challenges**?

**Audit emphases:**
- Name the **power asymmetry**: designers and employers hold power over candidates who often can't opt
  out, see their data, or contest a score.
- Flag **cross-cultural transfer**: a model trained/validated in one cultural or linguistic context
  and deployed in another (different dialects, norms, nonverbal behavior) without adjustment is a
  high-risk finding. Connects to feature-engineering choices (dialect/NLP) in
  `ai-model-development-audit` and to linguistic/cultural equivalence in
  `candidate-accommodations`.
- Ask whether affected **communities participated** in design — absence is itself a finding.

## Component 11 — Respect (conformance to ethical standards)

**What it is:** whether the algorithm is developed and used in conformance with **generally accepted
ethical standards.**

**Questions to ask:** Does its use conform to accepted ethical standards — e.g., the **Standards**, the
**SIOP Principles**, the **OECD Principles on AI**, and the **UGAI**?

**Apply it (focal example):** What ethical standards do the developers **claim** to have followed? Is
there **evidence of decisions actually made** following that framework? What evidence is there that
**individual fairness was a priority** during development?

**Audit emphases:**
- Distinguish **professed** standards from **demonstrated** adherence — require traceable decisions,
  not a values statement.
- For psychologists, the **APA Ethics Code** binds the work (beneficence/nonmaleficence,
  fidelity/responsibility, integrity, **respect for rights and dignity**, **justice/minimizing one's
  own bias**) — see `ai-fairness-lenses`, Lens 2.
- AI-specific codes (OECD, UGAI) reference fairness/reliability/validity but **don't define them
  precisely** — so "we follow UGAI" is not self-certifying; check what was actually done.

## Component 12 — Research designs

**What it is:** the **methodological quality of the studies** offered to support *any* claim — the
integrity check underneath everything.

**Questions to ask:** How do the **research designs** (sampling, experimental design, variable choices,
analysis, interpretation) of any supporting studies **affect the validity of the conclusions**?

**Apply it (focal example):** For **every claim that appears to rest on empirical observation**, does
the **study design support the claim**? Were all design decisions **defensible from the perspective of
modern methodological research**? What **impact** might they have had on the validity of the
conclusions?

**Audit emphases:**
- This is where the auditor applies standard **research-methods scrutiny** to the developer's own
  validation studies: sampling adequacy, confounds, appropriate analyses, defensible interpretation.
- It pairs with the **psychometric** evaluation in `ai-model-outputs-audit` — Component 12 asks whether
  the *study that produced* the validity/reliability evidence was itself sound.
- An audit's own credibility also rests here: **failing to articulate the standards** by which the
  audit was conducted can make its results uninterpretable.

## Pitfalls

- Treating culture/ethics/research-integrity as an afterthought instead of cross-cutting checks.
- Accepting a values statement as proof of ethical adherence.
- Missing cross-cultural transfer risk for a model moved between contexts.
- Auditing reported results without auditing the **design** that produced them.
- Ignoring power differentials that prevent candidates from contesting or understanding decisions.

## Checklist

- [ ] Power differentials among designers/employers/candidates named
- [ ] Cultural assumptions and cross-cultural transfer risks evaluated; adjustments verified
- [ ] Community participation in design assessed
- [ ] Claimed ethical frameworks (Standards/Principles/OECD/UGAI/APA Ethics) identified
- [ ] Demonstrated (not just professed) adherence evidenced via traceable decisions
- [ ] Every empirical claim's underlying study design scrutinized for methodological defensibility
- [ ] Audit's own fairness/measurement standards articulated for interpretability

## See also

`ai-fairness-lenses` (legal/ethical/moral lens) · `ai-audit-planning` · `ai-model-outputs-audit`
(psychometric counterpart to Component 12) · `ai-audit-reporting` ·
`candidate-accommodations` (linguistic/cultural equivalence)

*Source: Landers & Behrend (2023), Table 1 (Components 10–12, "Meta-components").*