ai-claims-and-stakeholder-audit

name	ai-claims-and-stakeholder-audit
description	Use when auditing how an AI/ML personnel assessment is described and how it affects people — Components 7-9 (information & perceptions) of the Landers & Behrend (2023) framework. Covers first-party developer claims (do they honestly and transparently follow from the audit evidence?), second-party effects on those assessed (candidate reactions, justice, false positives vs. false negatives, what is communicated), and third-party understanding (employment-law experts, regulators, community, public). Triggers: "developer marketing claims vs evidence", "candidate reactions to AI hiring", "applicant fairness perceptions", "false positive vs false negative impact", "what do regulators/public think", "transparency of AI hiring claims".
version	1.0.0
author	OpenMatter-Network
license	MIT
category	research
tags	["Community","io-psychology","ai-assessment","auditing"]
permissions	[]

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").