--- title: "Responsible AI and European Compliance" description: "Learn how the EU AI Act and GDPR apply to AI systems, and how to build transparent, explainable, and privacy-preserving AI on STACKIT." scfAsset: managed: false category: "policy" maintainers: - user: "can.celik1" external: true tags: ["STACKIT University", "Learning", "EU AI Act", "GDPR", "Responsible AI", "Bias"] source_url: "https://framework.stackit.cloud/data-and-ai/assetcontainer/stackit/responsible-ai-and-european-compliance/" source_file: "docs/data-and-ai/assetcontainer/stackit/responsible-ai-and-european-compliance.mdx" --- ## Course Overview This course covers the legal and ethical guardrails for deploying AI in Europe, starting with the EU AI Act's risk-based classification system — what counts as a prohibited practice, what counts as high-risk, and how that interacts with GDPR — before moving into the specifics of Article 22 and automated decision-making, Data Protection Impact Assessments, and the legal bases you need to process data in an AI system at all. From there it turns practical: implementing explainable AI (XAI) techniques, measuring and mitigating bias with real fairness metrics, and applying privacy-preserving methods like differential privacy and federated learning so that compliance is built into the system's design rather than bolted on afterward. This isn't abstract policy reading — it's the compliance foundation the capstone course later expects you to defend in front of an actual compliance officer, and it's essential for anyone deploying AI systems that touch European data or European markets. ### What You'll Learn - Classify an AI system under the EU AI Act's risk tiers and identify prohibited practices - Apply GDPR Article 22 and conduct a Data Protection Impact Assessment for an AI system - Measure bias with fairness metrics and implement explainable AI techniques - Apply privacy-preserving methods such as differential privacy and federated learning ### Modules - European AI Regulatory Landscape - GDPR and AI Systems - Transparency, Explainability, and Fairness - Privacy-Preserving AI and Best Practices