Implements AI transparency requirements under EU AI Act Arts. 13-14 and GDPR Arts. 13-14. Covers user notification of AI interaction, system capability disclosure, limitation documentation, and meaningful information about automated logic. Keywords: AI transparency, EU AI Act, GDPR notification, explainability, automated decision.
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Implements AI transparency requirements under EU AI Act Arts. 13-14 and GDPR Arts. 13-14. Covers user notification of AI interaction, system capability disclosure, limitation documentation, and meaningful information about automated logic. Keywords: AI transparency, EU AI Act, GDPR notification, explainability, automated decision.
AI transparency operates at the intersection of two regulatory frameworks: the GDPR's data subject information rights (Arts. 13-14) and the EU AI Act's transparency obligations (Arts. 13-14, 50). Together they require controllers and deployers to provide meaningful, accessible information about AI system capabilities, limitations, decision logic, and personal data processing. This skill implements the combined transparency framework, addressing both the technical explainability challenge of complex ML models and the legal obligation to communicate AI processing in plain language to affected individuals.
GDPR Transparency for AI Systems
Art. 13-14 Information Requirements Applied to AI
When personal data is processed by AI systems, data subjects must receive:
Information Element
GDPR Article
AI-Specific Application
Purposes of processing
Art. 13(1)(c) / 14(1)(c)
Specific AI use case, not generic "service improvement"
Lawful basis
Art. 13(1)(c) / 14(1)(c)
The basis for AI training and for AI inference separately
Legitimate interest
Art. 13(1)(d) / 14(2)(b)
The specific interest served by AI processing
Recipients
Art. 13(1)(e) / 14(1)(e)
AI infrastructure providers, model hosting services
International transfers
Art. 13(1)(f) / 14(1)(f)
Where AI processing occurs (training and inference locations)
Retention period
Art. 13(2)(a) / 14(2)(a)
Training data retention, inference log retention, model lifecycle
Data subject rights
Art. 13(2)(b) / 14(2)(c)
Including AI-specific rights: explanation, contestation, human review
Automated decision-making
Art. 13(2)(f) / 14(2)(g)
Meaningful information about logic, significance, and envisaged consequences
Source of data
Art. 14(2)(f)
Training data sources (categories, not necessarily individual sources)
Art. 13(2)(f) / 14(2)(g) — Meaningful Information About Automated Logic
This is the most challenging transparency requirement for AI systems. The EDPB and Article 29 Working Party have clarified:
What "meaningful information about the logic involved" requires:
System-level explanation: General description of how the AI system works — what factors are considered, what methodology is used, how the model was trained
Purpose and context: Why the AI system is used and what role its output plays in decisions
Key variables: The main data points or features that influence the AI output, without requiring disclosure of proprietary algorithms
Decision criteria: How the model output translates into a decision (e.g., score thresholds, classification categories)
Significance: What the decision means for the data subject in practical terms
Consequences: The potential effects (both intended and foreseeable) of the AI-driven decision
What it does not require:
Full disclosure of source code or model weights
Mathematical description of the algorithm
Proprietary trade secrets (but this does not exempt from meaningful explanation)
Explanation of individual model predictions in real-time (though this may be required under Art. 22)
Layered Approach to AI Transparency
The EDPB recommends a layered transparency approach:
Layer
Content
Delivery
Layer 1: Initial notice
AI is used in processing; general purpose; link to full information
At point of interaction (banner, tooltip, notification)
Layer 2: Summary
AI system description, key data used, decision logic summary, rights available
Privacy notice section, AI information page
Layer 3: Detailed information
Full technical description, training data categories, fairness measures, accuracy metrics, limitations
Supplementary documentation, upon request
Layer 4: Individual explanation
Specific factors influencing a particular decision, appeal mechanism
Upon request or automatically for significant decisions
EU AI Act Transparency Obligations
Art. 13 — Transparency and Provision of Information to Deployers (High-Risk)
High-risk AI systems (Annex III) must be designed and developed to ensure:
Requirement
Description
Interpretability
System design enables deployers to interpret outputs and use them appropriately
Instructions for use
Detailed documentation of capabilities, limitations, intended purpose, foreseeable misuse
Performance metrics
Accuracy levels, robustness metrics, known limitations for specific groups
Human oversight info
Description of human oversight measures and how to implement them
Input data specs
Description of input data the system was designed to process
Training data description
Relevant information about training data including provenance and preprocessing
Art. 14 — Human Oversight (High-Risk)
High-risk AI systems must be designed to enable effective human oversight:
Clear indication of AI system outputs and confidence levels
Ability to correctly interpret AI outputs in context
Ability to override or reverse AI decisions
Ability to intervene or stop the system ("stop button")
Awareness of automation bias risk
Art. 50 — Transparency for Specific AI Systems
AI System Type
Transparency Obligation
AI interacting with persons
Inform that they are interacting with an AI system (unless obvious from context)
Emotion recognition / biometric categorisation
Inform about the system's operation and process personal data in compliance with GDPR
AI-generated or manipulated content (deepfakes)
Label content as AI-generated in a machine-readable format
AI-generated text on matters of public interest
Disclose that the text has been artificially generated or manipulated
Art. 50(1) — AI Interaction Notification
Controllers must inform natural persons that they are interacting with an AI system. This applies to:
Chatbots and virtual assistants
AI-powered customer service systems
Automated email or message generation
AI-driven recommendation systems with direct user interface
Voice-based AI assistants
Exceptions: where it is obvious from the circumstances and context that the person is interacting with AI (e.g., a robot in a factory setting).
AI Transparency Documentation Framework
Model Card Requirements
For each deployed AI model, maintain a model card containing:
Section
Content
Model overview
Name, version, type, developer, deployment date
Intended use
Specific purpose, target users, deployment context
Out-of-scope use
Uses the model is not designed for; foreseeable misuse
Training data summary
Data sources (categories), volume, temporal range, geographic scope, known biases
Performance metrics
Accuracy, precision, recall, F1 by relevant subgroup; fairness metrics
Limitations
Known failure modes, demographic performance disparities, edge cases
Privacy properties
Differential privacy applied (epsilon), membership inference test results, training data extraction risk
Human oversight
Level of oversight required, reviewer qualifications, override procedures
Update history
Retraining dates, data updates, performance changes
AI System Transparency Register
Organisations operating multiple AI systems should maintain a central register:
Field
Description
System ID
Unique identifier
System name
Human-readable name
AI Act classification
Unacceptable / High / Limited / Minimal
Purpose
Specific processing purpose
Data subjects affected
Categories and estimated numbers
Personal data processed
At training and inference
Decision authority
AI decision-support vs. automated decision
Transparency measures
Notification, explanation, documentation
Deployer
Internal / External deployment
Registration date
EU AI Act database registration (if high-risk)
Explainability Techniques for Compliance
Global Explainability (System-Level)
Techniques for providing Art. 13(2)(f) "meaningful information about the logic":
Technique
Best For
Limitation
Feature importance (SHAP, LIME)
Identifying key variables
May oversimplify complex interactions
Decision rules extraction
Converting model logic to human-readable rules
Loss of accuracy for complex models
Partial dependence plots
Showing how features affect predictions
Assumes feature independence
Counterfactual explanations
Showing what change would lead to different outcome
Computationally expensive for many features
Attention visualisation
Transformer models — showing what the model focuses on
Attention does not always equal importance
Local Explainability (Individual Decision)
For Art. 22 right to explanation of individual decisions:
Technique
Description
Use Case
LIME
Local Interpretable Model-agnostic Explanations
Any model — approximate local behaviour with interpretable model
SHAP values
Shapley Additive Explanations for individual predictions
Feature contribution to specific prediction
Counterfactual
"You were denied because X; if X were Y, outcome would be different"
Credit, hiring, insurance decisions
Anchors
Sufficient conditions for a prediction
Rule-based explanation of individual case
Concept-based
High-level concepts that influenced the decision
When features are not directly interpretable
Enforcement Precedents
Garante v. OpenAI (2023): Required transparency about AI training data processing, model capabilities, and limitations — privacy notice deemed insufficient for AI system transparency.
CNIL v. Clearview AI (SAN-2022-019, 2022): EUR 20M fine — complete absence of transparency about facial recognition AI processing; data subjects had no notice their images were scraped and processed.
Austrian DPA v. CRIF (DSB-D213.636, 2023): Credit scoring AI — insufficient explanation of automated decision-making logic per Art. 13(2)(f); data subject received only a score without meaningful information about factors.
Dutch DPA v. Tax Authority (SyRI, 2020): Court found algorithmic fraud detection lacked transparency — citizens could not understand how the system assessed them, violating right to private life.
AEPD v. CaixaBank (PS/00421/2020, 2021): EUR 6M fine — automated credit decision-making without adequate transparency about the logic involved and significance for data subjects.
Integration Points
ai-automated-decisions: Art. 22 explanation requirements integrate with transparency obligations
ai-dpia: Transparency assessment is part of DPIA necessity and proportionality analysis
ai-act-high-risk-docs: Art. 13 AI Act documentation requirements overlap with transparency framework
ai-deployment-checklist: Pre-deployment transparency validation is a checklist item
