EU AI Act: What You Must Know
EU AI Act: What You Must Know (3 days)
The EU AI Act is the world’s first comprehensive AI law—already in force since 1st August 2024 with phased obligations through 2026–2027. It applies extraterritorially: if you place AI systems or GPAI models on the EU market, use (deploy) AI in the EU, or your AI’s outputs are used in the EU, you’re in scope—even if your company is not based in the EU. The Act’s goal is to keep AI safe, trustworthy, and rights-respecting while supporting innovation across the single market.
The EU AI Act is a risk-based regulatory framework that sets obligations for providers and deployers, aligned to risk tiers (prohibited, high, limited, minimal). It was created to protect health, safety, and fundamental rights, ensure a level playing field in the EU market, and foster trustworthy AI innovation.
Current status at a glance
- in force since 1st August 2024;
- staged application (e.g., prohibitions and AI-literacy obligations from 2nd February 2025;
- GPAI & governance from 2nd August 2025;
- most rules fully applicable by 2nd August 2026;
- certain embedded high-risk systems by 2nd August 2027
Course promise
Get a concise, practical introduction to the EU AI Act—what it is, why it matters, and how to act now. In one day, you’ll master the Act’s structure, scope, and risk tiers; learn who’s affected and how typical use cases (from HR to copilots) are classified; and translate legal duties into concrete controls, documentation, and governance. We cover transparency, safety, and post-market monitoring, plus liability and sanctions—then put it all to work in a hands-on policy lab where you draft a one-page AI policy for your organization. Ethics and strategy are woven throughout, so you leave with a clear roadmap to operationalize AI compliance. Designed for both technical and non-technical professionals.
Course Outline
1) Legal Framework: EU AI Act
- Scope & aims: What’s covered, key roles (provider/deployer/etc.), GPAI vs. application AI, timelines & penalties.
- Who’s affected: Business functions, in-house vs. vendor solutions, marketplace & open-source.
- Risk tiers: Prohibited / high / limited / minimal; quick triage by purpose, context, impact, safeguards.
- Use-case mapping: HR screening, credit scoring, industrial vision, chatbots/RPA/copilots; grey zones (monitoring, emotion/biometrics).
- Accountability & implementation: Role mapping across product/data/IT/legal/risk; assurance gates from policy to production.
2) Safety & Conformity Measures
- Transparency & documentation: User info, system transparency, content labeling; evidence pack (data governance, tech docs, logs, tests).
- Governance & controls: Minimal AI policy (scope/roles/escalation), control library (data quality, robustness, oversight, cybersecurity), change/version control, post-market monitoring.
- Liability & sanctions: Fines, product-liability touchpoints, contracting with vendors/integrators.
- Hands-on lab — Company AI Policy: 1) Classify two use cases → 2) Select controls & transparency → 3) Draft a one-page policy
3) Ethics, Responsibility & Strategy
- Enterprise ethics: Fairness, bias, explainability vs. performance; practical human oversight.
- Societal impact: Workforce, accessibility, trust; content integrity & IP.
- Operationalizing compliance: Shift-left in product/ML pipelines; KPIs & audits (drift, incidents, complaints, effectiveness).
- Case studies: What works (early classification, lean docs, red-teaming) vs. what fails (undocumented data, unclear ownership, checkbox oversight).
Learning Outcomes
- Classify your AI use cases and map them to obligations
- Draft a lightweight AI policy and control set for your organization
- Define documentation that stands up to scrutiny
- Design a simple but durable AI governance workflow
- Spot liability hotspots and contract for risk
Who should attend
- Business leaders, product owners, and project managers introducing AI in products or processes
- Compliance, legal, risk, and data protection teams
- Data, Machine Learning, Data Science and IT professionals who must align development with regulatory requirements
- Human Resources & Learning & Development leaders setting up AI competence programs company-wide
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