MEASURE-3.3: Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics.

MEASURE-3.3 requires you to stand up real, accessible feedback and appeal channels for end users and impacted communities, then feed what you learn back into how you measure and monitor AI performance. Operationally, that means a documented intake-to-resolution workflow, defined metrics (volume, severity, turnaround, uphold/overturn rates), and a governance loop that drives model evaluation and change decisions. ¹

Key takeaways:

• Build feedback and appeals as an end-to-end process (intake, triage, investigation, decisioning, closure), not an email inbox.
• Integrate feedback outcomes into AI evaluation metrics and recurring monitoring, not just incident logs. ¹
• Retain evidence that proves the process works in practice: tickets, decisions, metric reports, and change records tied to model updates.

A feedback and appeals program is one of the fastest ways to detect harm that your pre-deployment testing missed: biased outcomes, accessibility barriers, confusing explanations, or failure modes that only show up in live use. MEASURE-3.3 pushes you to treat these signals as first-class measurement inputs, not “customer service” noise. The requirement is explicit that feedback must be available to “end users and impacted communities,” which is broader than your direct customers and can include people subject to an AI-driven decision without a direct relationship to your organization. ¹

For a Compliance Officer, CCO, or GRC lead, the operational challenge is predictability: you need a repeatable way to accept reports, authenticate and protect sensitive information, route issues to the right owners, make appeal decisions with human accountability, and then quantify outcomes so they affect model evaluation and risk reporting. This page gives you requirement-level implementation guidance you can implement quickly: the minimum process design, what to measure, what to retain for audit readiness, and where teams typically fail.

Primary source references: NIST AI RMF Core and the NIST AI RMF program page. ²

Regulatory text

Excerpt: “Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics.” ¹

What the operator must do (operator translation):

1. Establish feedback channels that are actually usable by end users and impacted communities (not only internal testers or business customers). ¹
2. Establish an appeal pathway for people to challenge AI-influenced outcomes (for example, a denial, ranking, eligibility determination, moderation action, or routing decision). ¹
3. Integrate the resulting signals into measurement by defining metrics, tracking them, and using them in evaluation and monitoring routines that inform whether the system is performing acceptably and whether changes are required. ¹

Plain-English interpretation

MEASURE-3.3 is asking for two things, plus proof they matter:

• A “voice of the impacted” mechanism: People affected by the system can report problems (bugs, harmful outputs, unfair treatment, confusing decisions, unsafe behavior, accessibility issues).
• A “second look” mechanism: People can appeal outcomes, and you have a defined method to reconsider outcomes with human accountability where appropriate.
• A metrics integration loop: The feedback and appeal outcomes are measured and reviewed as part of AI system evaluation, not handled as one-off complaints. ¹

If your organization cannot show that feedback changes evaluation metrics and governance decisions (for example, risk ratings, monitoring thresholds, retraining triggers, or release gates), you are only partially meeting the requirement.

Who it applies to

Entities: Any organization developing, procuring, integrating, or deploying AI systems, including systems embedded in third-party products you configure or operate. ¹

Operational contexts where auditors will care most:

• AI that influences eligibility, access, or allocation (credit, housing, employment workflows, insurance, benefits, education).
• Safety-impacting AI (healthcare triage support, industrial controls, automotive assistance, critical infrastructure monitoring).
• AI used for content moderation, identity verification, fraud detection, risk scoring, or ranking where outcomes can be contested.
• AI deployed via third parties where your organization still owns customer outcomes, customer support, or regulatory accountability.

What you actually need to do (step-by-step)

1) Define scope: which systems get feedback and appeals

Create an inventory view (or extend your AI inventory) with:

• System name, owner, purpose, decision points, and user populations
• Whether the system produces appealable outcomes (and what “appeal” means operationally)
• Which communities are “impacted” beyond direct users (for example, applicants, dependents, bystanders, or subjects of surveillance)

Output: a scoped list of AI systems where MEASURE-3.3 applies, plus rationale. ¹

2) Design feedback intake channels that real people can use

Minimum viable channels typically include:

• A web form linked from product UI or decision notices
• A dedicated email alias with ticketing integration
• In-product “report a problem” for interactive systems
• A non-digital option if your user base requires it (for example, mail or phone routed into the same ticket system)

Operational requirements:

• Publish clear categories (harmful output, incorrect decision, discrimination concern, privacy/security, accessibility, other)
• Collect enough context to investigate (timestamps, screenshots, reference IDs, model version if available)
• Provide an acknowledgement and a way to track status

Treat this as a controlled process with ownership, not ad hoc support. ¹

3) Build an appeals workflow with decision authority

Document an “appeal” as a distinct case type with:

• Eligibility: which outcomes can be appealed and within what window (your policy choice)
• Required inputs: what the appellant must submit, what you can accept, and what you cannot request
• Routing and independence: who reviews appeals (avoid the same individual or team that is measured on the original decision outcome when feasible)
• Human accountability: when a human must review; define escalation triggers (high severity harm, repeated issues, protected-class claims, safety issues)
• Outcome codes: upheld, overturned, modified, insufficient information, out of scope
• Communication templates: decision explanation that is understandable and consistent

Key control: “No appeal disappears.” Every appeal ends in a coded disposition. ¹

4) Define evaluation metrics that incorporate feedback and appeals

You need metrics that connect user/community signals to model evaluation. Use a balanced set:

Operational metrics (process health)

• Volume of feedback and appeals by system, channel, and category
• Time-to-triage, time-to-close
• Backlog size and aging

Outcome metrics (model and decision quality)

• Appeal overturn rate by outcome type
• Repeat issue rate for the same failure mode
• Severity-weighted harm rate (define severity levels internally)
• Disparity signals where you can lawfully measure them (for example, differential appeal uphold rates by segment where permitted)

Change metrics (did you act)

• Percentage of high-severity issues linked to corrective actions (policy change, model update, guardrail update, UI change)
• Post-fix recurrence for the same root cause

Your exact metric choices can vary; the requirement is that feedback/appeals are integrated into AI evaluation metrics and reviewed in governance. ¹

5) Integrate into your AI evaluation cadence and release gates

Make the metrics operational by inserting them into existing routines:

• Model monitoring reviews (regular risk/quality reviews)
• Pre-release sign-off (no release if open critical appeal themes exist)
• Incident response (feedback spikes trigger investigation)
• Periodic risk assessments and control testing

A practical mechanism: a standing “AI Issues & Appeals Review” agenda item with documented minutes and action items assigned to model owners and product owners. ¹

6) Close the loop: root cause analysis and corrective action

For material feedback and appeals, require:

• Root cause classification (data issue, model behavior, prompt/guardrail failure, UX confusion, policy misalignment, human-in-the-loop error, third-party component)
• Corrective action plan with owner and due date
• Validation step (test cases, offline eval, shadow testing, or monitoring thresholds updated)
• Communication back to reporting party when appropriate

This is the “integrated” part auditors look for: the metric must change what you do next. ¹

7) Assign owners and evidence collection (audit readiness)

Follow the recommended control pattern: map MEASURE-3.3 to a policy, procedure, control owner, and recurring evidence collection. ¹

If you use Daydream, treat this as a control in your compliance workspace with:

• A named control owner (Product, Risk, or Compliance)
• A recurring evidence task for metric exports, sample case files, and governance minutes
• A system-level mapping so each AI system has its own feedback/appeals proof set

Required evidence and artifacts to retain

Maintain these artifacts per in-scope AI system:

1. Policy / standard
   • “AI Feedback and Appeals Standard” defining scope, roles, SLAs (your choice), and reporting channels
2. Procedure / workflow documentation
   • Intake-to-resolution flow, escalation criteria, appeal decisioning steps
3. Public-facing disclosures
   • UI text, decision notices, help center pages describing how to report problems and appeal outcomes
4. System evidence (operating effectiveness)
   • Ticket exports showing categories, timestamps, dispositions
   • Sample of closed feedback cases and appeal cases (redacted)
   • Decision rationale templates used in appeal closures
5. Metrics and monitoring outputs
   • Periodic metrics report/dashboard snapshots
   • Trend analysis and thresholds/alerts (if used)
6. Governance artifacts
   • Meeting minutes where metrics are reviewed
   • Action items and follow-up evidence
   • Change records linking issues to model/data/policy changes
7. Third-party coordination records (if applicable)
   • Contracts or support runbooks showing how feedback/appeals involving third-party components are handled

Common exam/audit questions and hangups

Auditors and internal reviewers tend to ask:

• “Show me where an impacted person can submit a complaint without knowing your internal terminology.”
• “Which outcomes are appealable, and who has authority to overturn them?”
• “How do you prevent the appeals process from being a dead end?”
• “Where do these signals show up in your model evaluation metrics or risk reporting?” ¹
• “Provide examples where feedback led to a model change, guardrail update, or policy change.”
• “How do you handle feedback from non-customers who are still impacted?” ¹

Hangups:

• Teams can show a support inbox but cannot show metrics integration.
• Appeals exist “on paper” but lack coded outcomes, so you cannot trend them.

Frequent implementation mistakes and how to avoid them

1. Mistake: Treating feedback as generic customer support.
   Fix: Tag AI-related issues explicitly and require a root cause field tied to the AI system and version.

2. Mistake: No appeal path for automated or AI-influenced decisions.
   Fix: Define appealability by decision type, publish it, and create an appeal case type with dispositions. ¹

3. Mistake: Metrics that measure speed only.
   Fix: Add outcome and change metrics (overturn rate, recurrence, corrective action linkage).

4. Mistake: Impacted communities excluded because they are not “users.”
   Fix: Expand intake options and update notices so affected non-users can report and appeal. ¹

5. Mistake: No governance loop.
   Fix: Put feedback/appeals metrics into a standing review and connect the outputs to release gates and monitoring thresholds.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement, so you should treat MEASURE-3.3 as a control expectation and audit-readiness standard rather than a directly cited enforcement item. ¹

Risk implications if you do not implement MEASURE-3.3 well:

• You will miss real-world harm signals and discover issues only after escalation through regulators, media, or litigation.
• You will have weak defensibility: you cannot show that your monitoring reflects user/community experience or that you offer meaningful recourse.
• In third-party AI deployments, you may be unable to prove you exercised appropriate oversight over outcomes your organization delivers.

A practical 30/60/90-day execution plan

First 30 days (foundation)

• Identify in-scope AI systems and appealable outcomes; assign system owners.
• Stand up intake channels (web form + ticketing) and define categories/severity.
• Draft and approve the “AI Feedback and Appeals Standard” and workflow.
• Define initial metric set and reporting format; build a basic dashboard/export.

By 60 days (operationalize)

• Train support, trust & safety, and risk teams on triage and escalation.
• Pilot appeals workflow on one high-impact system; require coded dispositions.
• Start governance review cadence; document minutes and action items.
• Implement a corrective action workflow linking cases to model/policy/UX change tickets.

By 90 days (embed into evaluation)

• Expand to all in-scope systems; publish consistent reporting/appeal instructions.
• Add trend analysis and thresholds that trigger investigation.
• Demonstrate closed-loop examples: at least one feedback theme and one appeal theme that produced evaluation updates and corrective actions, with evidence retained.
• Set recurring evidence collection in Daydream (or your GRC tool) so you can answer audits without a scramble. ¹

Frequently Asked Questions

Do we need an appeals process for every AI output?

No. Define which outcomes are appealable based on impact and decision context, then document the boundary clearly and publish it where affected people will see it. For non-appealable outputs, you still need a way to report problems. ¹

Who counts as “impacted communities” if they are not our customers?

Anyone materially affected by the system’s outputs or downstream decisions can be “impacted,” even without an account relationship. Provide an intake path that does not require being a logged-in user. ¹

What does “integrated into AI system evaluation metrics” mean in practice?

Your model evaluation and monitoring reports must include feedback and appeal metrics, and governance must review them as part of performance acceptability decisions. A separate customer support report with no tie to model evaluation is not enough. ¹

Can a third party run the intake and appeals process for us?

A third party can operate parts of the workflow, but you still need clear accountability, access to metrics, and the ability to drive corrective actions on the AI system you deploy. Retain evidence that shows oversight and closure. ¹

How do we prevent abuse of the feedback and appeals channels?

Use triage categories, rate limiting where appropriate, and identity verification only when necessary for the decision context. Do not design abuse controls that block legitimate reporters from submitting or tracking cases.

What evidence is most persuasive in an audit?

A small set of complete case files (intake, investigation notes, disposition, communications), plus a metrics report showing trends and a governance record showing decisions and resulting corrective actions. ¹

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF Core; Source: NIST AI RMF program page