ai-audit-reporting

name	ai-audit-reporting
description	Use when writing up and releasing the results of a psychological audit of an AI/ML personnel assessment — producing a precise, comprehensive technical report for testing professionals AND a layperson-friendly summary for those the predictions affect, establishing the auditor's standards and credibility in the report, and deciding on public release. Triggers: "write the AI audit report", "release the bias audit results", "dual-audience audit report", "should we publish the audit", "auditor credibility statement", "communicate algorithm audit findings".
version	1.0.0
author	OpenMatter-Network
license	MIT
category	research
tags	["Community","io-psychology","ai-assessment","auditing"]
permissions	[]

AI audit reporting

An audit only creates value if its results are communicated to the right audiences and, where the public interest is at stake, released. Reporting is also where the auditor's own credibility is established or lost.

Produce results in multiple formats (dual-audience)

Present results in multiple formats to meet the needs of all relevant audiences. At minimum:

1. A precise, comprehensive technical report for review by testing professionals — detailed enough that another competent professional could evaluate and, ideally, reproduce the audit's reasoning. Mirror the structure of a technical-validation-report, but organized around the 12 components and the claims evaluated.
2. A layperson-friendly summary for whomever the audit's predictions will directly affect (e.g., candidates) — clear, accurate, non-technical, addressing the fairness concerns in the terms those audiences actually use (justice/transparency — see ai-fairness-lenses).

One format cannot serve both audiences; write both.

Suggested technical-report structure

• Claims evaluated — each developer claim as an explicit statement, classified as validity / utility / lack-of-bias (from ai-audit-planning).
• Fairness standard(s) — the lens(es) and precise definitions used (ai-fairness-lenses), stated so conclusions are interpretable across disciplines.
• Findings by component (1–12) — model (1–6), information & perceptions (7–9), meta (10–12), each with evidence, gaps, and access limitations encountered.
• Auditor credibility statement (see below).
• Recommended corrective actions — to remove or minimize adverse effects, and (for formative audits) to improve the model.
• Limitations — what could not be evaluated, and why (NDA, missing documentation, small intersectional subgroups, etc.).

Establish credibility in the report

No auditor or audit is automatically credible. "This system has been audited and is therefore credible" deserves skepticism. So the report must let readers judge the audit itself by disclosing:

• the measurement standards and definitions of bias/fairness applied, and that they were applied in due course across the components;
• the auditor's type and relationship to the developer (internal / external / independent);
• the terms of access and nondisclosure — including what could be withheld at the company's discretion (the existence and scope of withholding should itself be disclosed).

Access and documentation vary even within auditor type, so transparency about access is essential to interpreting the findings.

Decide on release — default to disclosure in the public interest

Unless there is a compelling, transparently stated reason not to, an audit whose results are in the public interest should be released. Organizations may choose otherwise, but doing so risks the credibility of the audit, the company that built the algorithm, and the auditors. Public-facing, transparent, open audits are especially warranted when a system has outsized societal impact.

Normalize routine auditing. Treat regular internal and external auditing as a public good that raises the probability algorithmic systems in general are valid, valuable, and fair — and that builds public trust. Formative auditing folded into development, with complete documentation, can diminish or even preclude the need for post-hoc audits.

Pitfalls

• Shipping only a technical report (affected people can't use it) or only a summary (professionals can't evaluate it).
• Implying "audited = trustworthy" without exposing standards and access terms.
• Hiding what was withheld under NDA instead of disclosing that it was withheld.
• Suppressing a public-interest audit, harming everyone's credibility.
• Reporting findings without actionable corrective recommendations.

Checklist

• Technical report (component- and claim-structured) produced for professionals
• Layperson summary produced for affected audiences, in their fairness terms
• Fairness lens(es) and precise standards stated for interpretability
• Auditor type, relationship, and access/NDA terms disclosed (incl. what was withheld)
• Findings, gaps, and limitations reported honestly per component
• Corrective actions recommended (and, if formative, improvement guidance)
• Release decision made; public-interest default to disclosure applied and any non-release justified

See also

ai-audit-planning (audience & release policy set up front) · ai-fairness-lenses · all model/stakeholder/meta audit skills · technical-validation-report (structure parallel)

Source: Landers & Behrend (2023), "Designing an Effective Psychological Audit" — multiple-format reporting, releasing results in the public interest, normalizing routine auditing, and auditor credibility.