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Skillsdpia-automated-decisions

dpia-automated-decisions

Security & auditing

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Updated 10-16 09:33

About this skill

DPIA for Automated Decision-Making Systems

Purpose

Conduct a DPIA specifically tailored to automated decision-making (ADM) and profiling systems that produce legal or similarly significant effects on individuals, as required by GDPR Article 35(3)(a).

Prerequisites

  • System documentation for the ADM/profiling system (algorithm design, training data, decision logic)
  • Art. 35(3)(a) threshold confirmed: systematic and extensive evaluation of personal aspects based on automated processing, including profiling, on which decisions are based that produce legal effects or similarly significantly affect natural persons
  • Data categories processed identified, including any Art. 9 special categories
  • Existing Art. 22 safeguards documentation if available

Workflow

Step 1: System Description and Scope

Document the ADM system under assessment:

  1. System purpose: Business objective and processing purpose
  2. Decision types: What decisions the system makes or supports (credit scoring, insurance pricing, recruitment screening, fraud detection, content moderation)
  3. Processing scope: Data categories used as inputs, volume of data subjects affected, geographic scope
  4. Automation level: Fully automated (no human in loop), semi-automated (human in loop), or automated with human override capability
  5. Legal basis: Art. 6(1) basis for the processing, and if Art. 22 applies, the Art. 22(2) exception relied upon (contract, EU/Member State law, explicit consent)

Step 2: Necessity and Proportionality Assessment

Evaluate whether the automated processing is necessary and proportionate:

  1. Purpose limitation: Confirm processing purpose is specific, explicit, and legitimate per Art. 5(1)(b)
  2. Data minimization: Verify only data strictly necessary for the decision is processed per Art. 5(1)(c)
  3. Accuracy: Assess input data quality and its impact on decision accuracy per Art. 5(1)(d)
  4. Less intrusive alternatives: Document whether the decision could be made through less automated means
  5. Proportionality: Balance the organizational benefit against the impact on individuals' rights and freedoms

Step 3: Risk Identification

Identify risks specific to ADM systems:

  1. Discrimination risk: Assess whether the system could produce discriminatory outcomes based on Art. 9 characteristics (race, ethnicity, political opinions, religion, health, sexual orientation)
  2. Opacity risk: Evaluate whether the decision logic is explainable to affected individuals per Art. 13(2)(f) and Art. 14(2)(g)
  3. Accuracy risk: Assess false positive and false negative rates and their consequences
  4. Data quality risk: Evaluate whether training data reflects real-world populations without bias
  5. Feedback loop risk: Assess whether the system creates self-reinforcing biases
  6. Scope creep risk: Evaluate whether the system could be applied beyond its intended purpose
  7. Security risk: Assess adversarial manipulation vectors (data poisoning, model extraction)

Step 4: Art. 22 Compliance Assessment

If the system makes solely automated decisions with legal or similarly significant effects:

  1. Art. 22(1) prohibition check: Confirm whether the default prohibition applies
  2. Art. 22(2) exception: Document which exception permits the processing:
    • (a) Necessary for entering into or performance of a contract
    • (b) Authorized by EU or Member State law with suitable safeguards
    • (c) Based on explicit consent
  3. Art. 22(3) safeguards: Verify implementation of mandatory safeguards:
    • Right to obtain human intervention from the controller
    • Right to express the data subject's point of view
    • Right to contest the decision
  4. Art. 22(4) special categories: If Art. 9 data is processed, confirm explicit consent under Art. 9(2)(a) or substantial public interest under Art. 9(2)(g) with suitable safeguards

Step 5: Transparency and Explainability

Assess transparency obligations:

  1. Pre-decision transparency: Art. 13(2)(f) / Art. 14(2)(g) require providing meaningful information about the logic involved, significance, and envisaged consequences
  2. Logic explanation depth: Document what level of explanation is provided:
    • General system functionality description
    • Key factors/features influencing decisions
    • Decision thresholds and their justification
    • Individual-specific explanation of a particular decision
  3. Recital 71 guidance: Verify right to explanation is implemented as per Recital 71 (right to obtain an explanation of the decision reached after assessment)

Step 6: Risk Mitigation Measures

Implement measures to mitigate identified risks:

  1. Bias testing: Regular testing for discriminatory impact across protected characteristics
  2. Human oversight: Define when and how human reviewers intervene
  3. Accuracy monitoring: Ongoing performance monitoring with defined acceptable thresholds
  4. Audit trail: Complete logging of inputs, model version, and outputs for each decision
  5. Data subject rights: Clear process for individuals to exercise Art. 22(3) rights
  6. Regular review: Periodic re-assessment schedule (at minimum annually)

Step 7: Stakeholder Consultation

Consult required parties per Art. 35(9):

  1. DPO opinion: Obtain DPO assessment per Art. 35(2)
  2. Data subjects: Where appropriate, seek views of data subjects or their representatives per Art. 35(9)
  3. Technical experts: Algorithm developers, data scientists, domain experts
  4. Legal counsel: Assessment of applicable laws beyond GDPR (EU AI Act, sector-specific regulation)

Step 8: Documentation and Outcome

Document the DPIA outcome:

  1. Risk matrix: Residual risk rating after mitigation measures
  2. DPO recommendation: Documented DPO opinion on whether processing can proceed
  3. Prior consultation: If residual risk remains high, document decision to consult supervisory authority per Art. 36
  4. Review schedule: Next mandatory review date and trigger events for early review

Verification

  • ADM system fully described with decision types, automation level, and data categories
  • Art. 22(2) exception documented with Art. 22(3) safeguards implemented
  • Discrimination risk assessed across Art. 9 protected characteristics
  • Transparency obligations met per Art. 13(2)(f) / Art. 14(2)(g)
  • Human intervention mechanism operational and tested
  • Contestation process documented and accessible to data subjects
  • DPO opinion obtained and documented
  • Review schedule established with clear trigger events