AI Literacy and EU AI Act Compliance Training

Introduces the EU AI Act's risk-based regulatory framework (Updated: Aug 2025); outlines the four risk levels for AI systems and the rationale behind a tiered compliance approach

Defines AI applications banned as 'unacceptable risk' under the AI Act (e.g., social scoring, manipulative AI, certain biometric surveillance); explains why these practices are prohibited and notes legal implications as of 2025

Identifies what qualifies as high-risk AI (critical sectors and use-cases); overviews compliance obligations for high-risk systems (e.g., rigorous risk management, oversight, documentation) and the timeline for these requirements

Explains the limited-risk category of AI; discusses transparency requirements (e.g., informing users they are interacting with AI, labeling AI-generated content such as deepfakes); outlines how organizations should comply with these obligations

Discusses AI systems that pose minimal risk; notes that these have no mandatory requirements under the AI Act; encourages adherence to voluntary AI codes of conduct and best practices (expected by late 2025) for responsible use

Explains the AI literacy mandate in Article 4 of the EU AI Act (Updated: Aug 2025); defines 'sufficient level of AI literacy' and describes who (providers, deployers, staff) is covered by this obligation effective 2025

Describes how organizations can meet Article 4 obligations; covers steps to build AI literacy programs, including identifying training needs by role and risk level, developing relevant AI education content, and integrating training into company culture

Covers strategies to evaluate and improve AI literacy over time; suggests measuring staff understanding of AI concepts, monitoring compliance with training requirements, and updating learning initiatives as AI technology and regulations evolve

Provides an overview of artificial intelligence and machine learning; explains how AI systems learn from data (training models, algorithms) and highlights common AI applications, establishing a foundation for understanding later compliance topics

Explores how biases can emerge in AI systems (from data or algorithms); provides examples of AI-driven discrimination; underscores the importance of recognizing and addressing bias to ensure fairness and regulatory compliance

Introduces the concept of explainable AI; discusses why making AI decisions transparent and interpretable is crucial for trust and compliance; overviews methods to improve AI explainability and communicate AI system limitations to stakeholders

Emphasizes the ethical need for fairness in AI outcomes; discusses how to prevent discrimination by AI systems; outlines approaches (policy and technical) to ensure AI decisions are equitable and uphold fundamental rights

Addresses privacy risks associated with AI systems; covers compliance with data protection laws (e.g., GDPR) when developing or using AI; highlights practices like data minimization, anonymization, and secure data handling to protect personal information

Explains how to perform a risk assessment for an AI system; includes identifying potential harms to safety, fairness, or privacy; evaluating risk likelihood and impact; and determining the system's risk category under the AI Act guidelines

Discusses how to mitigate identified AI risks; covers methods such as improving training data quality to reduce bias, implementing human oversight for high-stakes AI decisions, rigorous testing/validation of AI models, and continuous monitoring to ensure ongoing compliance

Covers setting up governance structures for AI within an organization; defines roles and responsibilities (e.g., AI ethics board, compliance officer); describes policies and oversight mechanisms to guide AI development and deployment in line with regulations

Outlines documentation duties under the AI Act; includes maintaining technical documentation for AI systems (design, purpose, data sources, performance metrics), keeping logs of AI system operations, and ensuring traceability for audits and compliance checks

Details how to handle AI-related incidents and failures; explains which incidents (e.g., safety breaches, legal violations) must be reported to regulators under the AI Act and within what timeframe; covers establishing an incident response plan and corrective actions

Key content to include in this lesson": "Advises business users (AI deployers) on their compliance duties; covers selecting AI systems that meet regulatory standards, using AI outputs carefully (awareness of limitations and bias), ensuring transparency to clients or customers when AI is used, and monitoring outcomes to catch potential issues

Focuses on the role of managers and executives in AI compliance; discusses establishing an AI compliance strategy and ethical culture, ensuring teams are trained (Article 4 literacy), allocating resources for compliance and risk management, and reviewing compliance reports and audits