ai-transparency-reqs

name	ai-transparency-reqs
description	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.
license	Apache-2.0
metadata	{"author":"mukul975","version":"1.0","domain":"privacy","subdomain":"ai-privacy-governance","tags":"ai-transparency, eu-ai-act, gdpr-notification, explainability, disclosure, automated-logic"}

AI Transparency Requirements

Overview

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:

1. System-level explanation: General description of how the AI system works — what factors are considered, what methodology is used, how the model was trained
2. Purpose and context: Why the AI system is used and what role its output plays in decisions
3. Key variables: The main data points or features that influence the AI output, without requiring disclosure of proprietary algorithms
4. Decision criteria: How the model output translates into a decision (e.g., score thresholds, classification categories)
5. Significance: What the decision means for the data subject in practical terms
6. 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