# Article 6 – Classification of AI Systems
#### **1. Four Risk Levels** AI systems are classified into **four categories** based on their risk potential:
- **Prohibited AI Systems:** AI practices that pose unacceptable risks and are banned. - Examples: Subliminal manipulation, exploitation of vulnerabilities, and social scoring systems by public authorities. - **High-Risk AI Systems:** Systems that significantly impact individuals' safety or fundamental rights. - Examples: AI used in biometric identification, critical infrastructure, education, employment, credit scoring, or healthcare. - **Limited-Risk AI Systems:** Systems requiring transparency obligations but not as tightly regulated as high-risk systems. - Examples: Chatbots, recommendation systems, and virtual assistants. - **Minimal-Risk AI Systems:** Systems with negligible risk, which are largely unregulated. - Examples: Entertainment AI, spam filters, and AI-powered games. ---
#### **2. Criteria for Classification** The classification process considers:
- **Sector of Application:** The domain where the AI is deployed (e.g., education, healthcare). - **Impact on Rights and Safety:** How the AI affects individuals' privacy, safety, or fundamental rights. - **Severity of Harm:** The potential damage caused by incorrect or biased outcomes. - **Autonomy of AI Decision-Making:** The level of human involvement or oversight in the AI's decisions. ---
#### **3. High-Risk Systems in Education** Educational AI systems are explicitly listed under **Annex III** of the EU AI Act as high-risk if they:
- Determine **student access to education** (e.g., AI used in admissions). - Influence **learning outcomes** (e.g., AI-powered grading systems). - Assess **skills or competencies** that significantly affect career prospects.
These systems must comply with strict regulations, including documentation, risk management, and transparency requirements.
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#### **4. Obligations for High-Risk Systems** For AI systems classified as high-risk, the following obligations apply:
- **Conformity Assessments:** Systems must pass pre-deployment evaluations for compliance. - **Risk Management:** Continuous risk assessment throughout the system's lifecycle. - **Data Requirements:** High-quality, representative, and bias-free datasets. - **Human Oversight:** Mechanisms to ensure human intervention when needed. - **Monitoring and Reporting:** Continuous monitoring of performance and reporting of incidents. ---
### **Implications for Stakeholders** #### **AI Developers**
- Must evaluate whether their system falls under high-risk categories. - Implement safeguards like robust testing and documentation.
#### **Educational Institutions**
- Ensure AI tools for admissions, grading, or skill assessment meet high-risk criteria. - Conduct regular audits to verify compliance with the EU AI Act.
#### **Regulators**
- Monitor the deployment of AI systems in high-risk areas, especially those influencing fundamental rights. - Enforce penalties for non-compliance. ---