# Ai Audit Meta Components > >- - **Type:** Skill - **Install:** `agentstack add skill-openmatter-network-agent-io-skills-ai-audit-meta-components` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [OpenMatter-Network](https://agentstack.voostack.com/s/openmatter-network) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [OpenMatter-Network](https://github.com/OpenMatter-Network) - **Source:** https://github.com/OpenMatter-Network/agent-io-skills/tree/main/ai-personnel-assessment/skills/ai-audit-meta-components ## Install ```sh agentstack add skill-openmatter-network-agent-io-skills-ai-audit-meta-components ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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").* ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [OpenMatter-Network](https://github.com/OpenMatter-Network) - **Source:** [OpenMatter-Network/agent-io-skills](https://github.com/OpenMatter-Network/agent-io-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-openmatter-network-agent-io-skills-ai-audit-meta-components - Seller: https://agentstack.voostack.com/s/openmatter-network - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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