# Ai Claims And Stakeholder Audit > >- - **Type:** Skill - **Install:** `agentstack add skill-openmatter-network-agent-io-skills-ai-claims-and-stakeholder-audit` - **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-claims-and-stakeholder-audit ## Install ```sh agentstack add skill-openmatter-network-agent-io-skills-ai-claims-and-stakeholder-audit ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # AI claims & stakeholder audit (Components 7–9) The model can be technically sound and still be **mis-described** or **harmful in use.** This category shifts from the model's internals to how information about it is **presented, understood, and experienced** by three parties. It leans heavily on the **individual-attitudes (justice) lens** — see `ai-fairness-lenses`. ## Component 7 — First-party interpretation (developer claims) **What it is:** the **messaging** the algorithm developer puts out about the model. **Questions to ask:** Does **all** messaging from the developer **logically, honestly, and transparently** follow from answers developed elsewhere in the audit? **Apply it (focal example):** Does the developer **claim the model predicts job performance**? **What evidence in the audit forms the basis** of that claim? Are **important details left out**? **Audit emphases:** - Cross-check every public/marketing/sales claim against the **model-audit findings** (Components 1–6). A claim unsupported by the evidence — or that omits material caveats (range restriction, k-fold-only validation, untested intersectional subgroups) — is a finding. - This is **informational justice** in action: transparency about *what is assessed* and *what is done with the data* shapes how fair the system is perceived to be. ## Component 8 — Second-party effects (those assessed) **What it is:** impact on the people **directly affected** by the algorithm's use (candidates). **Questions to ask:** **Who** is directly affected, and how have their **outcomes and reactions** been assessed? What is the **relative impact of acting on false positives versus false negatives** on second parties? **Apply it (focal example):** How do **non-selected applicants react** to learning the algorithm did not score them high enough to be selected? **What information is communicated** to them, and how do they **evaluate** that information? **Audit emphases — use justice theory (Lens 1):** - **Procedural justice** rules the developer may be violating: opportunity to perform, job-relatedness/ face validity (do candidates believe facial expressions predict performance?), **reconsideration/ appeal** (algorithmic decisions that can't be appealed), **two-way communication** (AI replacing human interaction), and **propriety** (some find AI decisions morally inappropriate). - **Distributive justice:** which rule (equality/need/equity) do affected people apply to the outcome? - **Interactional justice:** interpersonal (respect/dignity) and informational (adequate explanation, e.g., an explanatory video before data collection). - **False positives vs. false negatives are not symmetric for second parties.** A false negative (a qualified candidate wrongly screened out) harms the individual; weigh it explicitly against false positives rather than optimizing a single accuracy number. Tie this to the error-cost reasoning in `selection-decisions-and-scoring`. ## Component 9 — Third-party understanding (outside observers) **What it is:** how **outside observers** perceive and evaluate the system. **Questions to ask:** How have **perceptions and evaluation by outside observers** been assessed and incorporated? Have **outside regulatory groups and community organizations** been consulted? **Apply it (focal example):** How do **experts in employment law** view the documentation and performance of the algorithm? How does the **public** view this use of algorithms? **Audit emphases:** - Solicit **employment-law** review (differential prediction, disparate impact, differential treatment — Lens 2) and **community/regulatory** input, rather than assuming internal sign-off suffices. - Public trust is part of the value proposition; transparent, outward-facing evaluation raises it (and is a stated benefit of normalizing audits). ## Pitfalls - Auditing the model but never checking the **claims made about it** against the evidence. - Optimizing overall accuracy while ignoring the **asymmetric cost of false negatives** to candidates. - Treating candidate reactions as PR rather than **justice** evidence relevant to fairness. - No appeal/reconsideration path; no two-way communication. - Assuming legality without consulting employment-law experts or affected communities. ## Checklist - [ ] Every developer claim cross-checked against Component 1–6 findings; omissions flagged - [ ] Informational transparency (what is measured / what is done with data) assessed - [ ] Directly affected parties identified; reactions/outcomes assessed - [ ] Procedural, distributive, and interactional justice rules evaluated - [ ] False-positive vs. false-negative impacts weighed for second parties - [ ] What is communicated to non-selected candidates reviewed (incl. appeal/reconsideration) - [ ] Employment-law expert and community/regulatory perspectives obtained - [ ] Public-perception considerations documented ## See also `ai-fairness-lenses` (justice; legal lens) · `ai-audit-reporting` (communicating to these audiences) · `ai-audit-meta-components` · `fairness-and-bias-analysis` · `selection-decisions-and-scoring` (error costs) · `administration-documentation` (candidate communications, feedback) *Source: Landers & Behrend (2023), Table 1 (Components 7–9, "Components relating to information and perceptions").* ## 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-claims-and-stakeholder-audit - 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. Every listing is security-reviewed. Creators keep 70%.