AI Audit Meta Components

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").