# Decision explanation, human review and appeal route

- **Record type**: Control
- **Kind**: Process
- **Owner**: Customer operations lead
- **Frequency**: Once per AI system
- **Duties served**: 10

## What the control achieves

Gives people affected by a significant automated or AI-assisted decision a meaningful explanation, a way to correct the data behind it, and a route to have a person reconsider it.

## How it is typically implemented

For each decision point that has legal or similarly significant effects, the owner defines what the person is told after the decision: the main reasons, the data relied on and its sources, and how to challenge the outcome. Complaint and customer-service procedures are extended with an AI-decision challenge path staffed by people with the authority and information to change the result, and with target response times. Requests, outcomes and reversals are logged and reviewed, both to satisfy individual rights and to detect systematic problems that should feed back into the model or the oversight design.

## Evidence it produces

- Adverse-decision explanation template (disclosure_notice)
- AI decision challenge and human review procedure (procedure): Intake route, reviewer authority, response times and record-keeping for challenges.
- Challenge and reversal log (monitoring_record)

## Legal duties this control serves

- Provide contestability, supply-chain transparency and records (guardrails 7 to 9) — Australian Voluntary AI Safety Standard, Australia (supports): https://aipolicytracker.org/obligations/australia-vaiss-contestability-supply-chain-records
- Deployers must tell natural persons that a high-risk AI system is used in decisions about them — EU AI Act, European Union (supports): https://aipolicytracker.org/obligations/eu-ai-act-art-26-11-notice-to-affected-persons
- Deployers must explain individual decisions taken with high-risk AI on request — EU AI Act, European Union (satisfies): https://aipolicytracker.org/obligations/eu-ai-act-art-86-right-to-explanation
- Operators of high-impact AI must be able to explain outputs and the main criteria behind them — Framework Act on the Development of Artificial Intelligence and Establishment of a Foundation for Trust, South Korea (satisfies): https://aipolicytracker.org/obligations/south-korea-ai-basic-act-art-34-high-impact-ai-explanation-measures
- Respect the right to object to automated decision-making without human intervention — UAE PDPL, United Arab Emirates (satisfies): https://aipolicytracker.org/obligations/uae-pdpl-automated-decision-objection
- Provide appropriate transparency and explainability — UK AI regulation framework, United Kingdom (supports): https://aipolicytracker.org/obligations/uk-principles-transparency-explainability
- Provide routes to contest AI outcomes and seek redress — UK AI regulation framework, United Kingdom (satisfies): https://aipolicytracker.org/obligations/uk-principles-contestability-redress
- Apply safeguards to solely automated decisions with significant effects — ICO AI guidance, United Kingdom (satisfies): https://aipolicytracker.org/obligations/uk-ico-automated-decision-safeguards
- Notify consumers and explain adverse consequential decisions — Colorado AI Act, Colorado (United States) (satisfies): https://aipolicytracker.org/obligations/us-colorado-consumer-notice-and-adverse-decision-explanation
- Employers and employment agencies must let candidates request an alternative selection process or accommodation — NYC Local Law 144 (automated employment decision tools), New York (United States) (satisfies): https://aipolicytracker.org/obligations/us-new-york-city-local-law-144-alternative-process-request

## Standards clauses it corresponds to (clause numbers only)

- ISO/IEC 42001:2023: Annex A.8.2, A.8.3, A.9.2
- NIST AI RMF 1.0: MEASURE 2.9; GOVERN 5.1; MANAGE 4.1
- OECD AI Principles: Principle 1.3 Transparency and explainability

## MIT AI Risk Repository subdomains addressed

5.2, 1.1, 7.4

## Provenance

- **Record page**: https://aipolicytracker.org/controls/decision-explanation-and-appeal-route
- **Official source**: none recorded — this record is incomplete, see https://aipolicytracker.org/gaps
- **Review status**: pending review
- **Confidence**: high
- **Facts last confirmed**: never confirmed against the official source
- **Retrieved**: 2026-09-24
- **Licence**: https://creativecommons.org/licenses/by/4.0/

> This record is a structured summary with a link to the official text. It is not legal advice. Open the official source before relying on any date or duty. How current each record type must be is published at https://aipolicytracker.org/verification; what a record must carry at all is published at https://aipolicytracker.org/coverage.