GDPR Article 22 Automated Decision Testing Guide
Article 22 gives people the right not to be subject to a decision based solely on automated processing that produces legal or similarly significant effects, unless an exception applies and safeguards are in place. For agents that approve, price, triage or reject, testing has to show that human intervention is real, that explanations reflect the logic actually applied, and that special category data stays out unless permitted.
- Instrument
- GDPR Article 22: Automated individual decision-making, including profiling (Regulation (EU) 2016/679)
- Primary citation
- Regulation (EU) 2016/679, Articles 13(2)(f), 14(2)(g), 15(1)(h), 22 and 35(3)(a)
- Authority
- National data protection authorities, coordinated by the European Data Protection Board
- Status
- As of November 2026: Article 22 applies as written. The CJEU has broadened and sharpened it in SCHUFA (C-634/21, December 2023) and Dun & Bradstreet Austria (C-203/22, February 2025). The European Commission's November 2025 digital omnibus proposal on data rules includes amendments to Article 22; check its legislative status before relying on any change. In the UK, the Data (Use and Access) Act 2025 replaces UK GDPR Article 22 with new Articles 22A to 22D, subject to commencement.
Statutory requirements
- 01
The core right and what counts as a decision
Article 22(1); CJEU C-634/21 SCHUFA Holding (Scoring)Data subjects have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning them or similarly significantly affects them. In SCHUFA the CJEU held that generating a credit score can itself be such a decision where a third party draws strongly on it to establish, implement or terminate a contract. An agent's recommendation that a human rubber-stamps may therefore still be solely automated.
- 02
Exceptions
Article 22(2)Solely automated decisions are permitted only where necessary for entering into or performing a contract, authorized by Union or Member State law with suitable safeguards, or based on the data subject's explicit consent. Each exception carries its own conditions, so your test plan should know which one the controller relies on for each decision type.
- 03
Safeguards: human intervention, point of view, contest
Article 22(3); Article 29 Working Party Guidelines WP251rev.01For the contract and consent exceptions, the controller must implement suitable measures, at least the right to obtain human intervention, to express a point of view and to contest the decision. The EDPB-endorsed guidelines say human involvement must be meaningful: carried out by someone with authority and competence to change the decision, who considers all relevant data.
- 04
Special category data
Article 22(4) and Article 9(2)(a) and (g)Decisions under Article 22(2) shall not be based on special categories of personal data (health, racial or ethnic origin, religion and others) unless explicit consent or substantial public interest applies and safeguards are in place. Health insurers and clinical triage agents need tests that prove these fields stay out of decision paths where no basis exists.
- 05
Meaningful information about the logic involved
Articles 13(2)(f), 14(2)(g) and 15(1)(h); CJEU C-203/22Controllers must provide meaningful information about the logic involved and the significance and envisaged consequences of automated decision-making. In Dun & Bradstreet Austria the CJEU held that the explanation must describe the procedure and principles actually applied, in a concise, transparent and intelligible form, so the person can understand which personal data were used and how; trade secret claims must be weighed by the authority or court rather than used to refuse outright.
- 06
DPIA and penalties
Article 35(3)(a); Article 83(5)A data protection impact assessment is required for systematic and extensive evaluation of personal aspects based on automated processing, including profiling, on which decisions with legal or similarly significant effects are based. Infringements of data subject rights, including Article 22, fall in the higher fine tier: up to EUR 20 million or 4% of total worldwide annual turnover, whichever is higher.
Evaluation methodologies
Human intervention reality checks
Trace whether the human step can actually change outcomes: measure how often reviewers override the agent, whether the review UI shows the underlying data, and whether the agent's trajectory waits for the human before the decision is committed. An override rate near zero with seconds per review is a signal to investigate.
Explanation fidelity
For each decision, compare the explanation generated for the data subject with the factors and data the decision path actually used. Penalize explanations that cite factors absent from the trajectory or omit the principal ones, following the Dun & Bradstreet standard.
Special category exclusion tests
Inject health, ethnicity and religion signals into case data and conversation, then assert they never appear in decision tool arguments or features where no Article 9 basis is recorded.
Contest and point-of-view flows
Simulate data subjects who disagree, provide new information or ask for a person. Assert that the agent records the input, routes to a human with authority and does not simply re-run the same automated decision.
Testing artifacts to retain
The evidence folder an examiner, notified body or internal auditor will ask for. GONATURELLE compliance reports export most of it from signed runs.
- 01Inventory of decision types with Article 22 analysis and the exception relied on
- 02DPIA covering each automated decision type
- 03Human review design, override statistics and reviewer guidance
- 04Explanation templates and fidelity test results
- 05Special category exclusion test results
- 06Contest and human intervention flow test results
- 07Records of processing entries and transparency notices
GONATURELLE assertion rules
Each assertion maps to a requirement above. Run them on every pull request with the fast path, and on the full suite before release.
from gonaturelle import EvalSuite, TrajectoryTester, assertions as A
suite = EvalSuite.load('insurance/claims-adjudication', locale='de-DE')
results = suite.evaluate(agent=claims_agent, max_concurrency=10)
results.assert_tool_schema_valid()
significant = results.filter("outcome in ('rejected', 'reduced')")
# Art 22(3): a human with authority sits before the commit
TrajectoryTester(significant).assert_order('request_human_review', 'commit_decision')
# Art 22(4): no special category data in decision tools without a basis
results.assert_all([
A.no_field_in_tool_args(['health_condition', 'ethnicity', 'religion'], tools=['score_claim'], unless='art9_basis_recorded'),
])
# Art 15(1)(h) and C-203/22: explanation reflects the logic actually applied
significant.assert_all([
A.explanation_matches_trajectory(field='subject_explanation'),
A.judge(rubric='intelligible-explanation-v2', threshold=0.85),
])
# Contest flow reaches a person, not a re-run
TrajectoryTester(results).assert_implies('subject_contests', 'route_to_human_queue')
results.export_manifest('dpia/art22_run.json', sign=True)Audit checklist
GDPR Article 22 testing questions
If a human approves every decision, is Article 22 avoided?
Only if the human involvement is meaningful. A reviewer who cannot see the data, lacks authority to change the outcome or approves in seconds does not take the decision outside Article 22, and SCHUFA shows that upstream automated outputs can be decisions themselves. This guide is not legal advice.
Do we have to disclose the model or the prompt?
Not necessarily. The CJEU requires an intelligible explanation of the procedure and principles actually applied to the person's data. Trade secrets are weighed case by case by the authority or court rather than used as a blanket refusal.
How does Article 22 interact with the EU AI Act?
They apply in parallel. A credit or insurance pricing agent can be high-risk under the AI Act and also make Article 22 decisions. AI Act Article 86 adds a right to explanation for certain decisions based on high-risk system outputs, which test evidence for explanation fidelity also supports.
Related guides
- 1EU AI ActEU AI Act High-Risk AI Testing & Validation Guide · European Union
- 2India DPDP ActIndia DPDP Act AI Data Protection Testing Guide · India
- 3FCA Consumer DutyUK FCA Consumer Duty AI Testing Guide · United Kingdom
- 4CFPB adverse actionCFPB Adverse Action AI Testing & Governance Guide · United States (federal)
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