2024-11-18 AI Literacy

1. Introduction to AI and its Applications

Definition of AI: Basic concepts, including machine learning, neural networks, and natural language processing.

Applications of AI: Use cases in healthcare, finance, education, transportation, and beyond.

AI Capabilities and Limitations: Understanding what AI can and cannot do.

2. Overview of the EU AI Act

Scope of the Legislation: Applicability to AI systems and providers within the EU and those operating in its market.

Classification of AI Systems:

Prohibited AI practices (e.g., social scoring).

High-risk AI systems (e.g., in critical infrastructure, recruitment).

Limited and minimal-risk AI systems.

Key Obligations:

Risk management frameworks.

Data governance and quality requirements.

Documentation and record-keeping.

Transparency obligations for users and stakeholders.

Penalties for Non-Compliance: Understanding enforcement and penalties.

3. Ethical and Responsible AI

Core Ethical Principles:

Transparency.

Accountability.

Fairness and non-discrimination.

Privacy and security.

Human-Centric AI: Ensuring AI benefits society and respects fundamental rights.

Bias in AI: How to detect, mitigate, and prevent bias in AI systems.

Informed Consent: Designing AI systems that respect user autonomy and provide clear information.

4. Data Management and Privacy

Data Protection Standards:

General Data Protection Regulation (GDPR) alignment.

Data minimization and anonymization techniques.

Data Quality: Importance of unbiased and representative datasets.

Transparency: Explaining how data is used, processed, and stored.

5. Risk Assessment and Mitigation

Risk Management Frameworks:

Identifying risks across the lifecycle of an AI system.

Implementing measures to reduce harm.

Impact Assessments: Conducting AI impact assessments (aligned with GDPR and the AI Act).

Post-Deployment Monitoring: Continuous evaluation and improvement of AI systems.

6. AI Governance and Compliance

Internal Governance:

Setting up AI ethics committees.

Defining roles and responsibilities in organizations using AI.

Audits and Reporting:

Preparing for regulatory audits.

Creating compliance documentation.

Third-Party Oversight:

Managing vendors and suppliers of AI systems.

Ensuring AI supply chain accountability.

7. AI Transparency and User Education

Explainability in AI:

• How to build and communicate interpretable models.

• Ensuring users can understand AI decision-making processes.

Labeling AI Systems:

• Informing users when they interact with AI.

• Disclosing limitations and risks.

8. Legal Implications and Case Studies

Legal Cases: Analysis of past instances of AI misuse or compliance failures.

Best Practices: Real-world examples of compliant and ethical AI deployments.

Impact of Non-Compliance: Understanding the legal and reputational risks of ignoring EU AI laws.

9. Future of AI Regulation in the EU

Dynamic Nature of AI Laws: Anticipating updates and preparing for future regulatory changes.

Horizon Scanning: Identifying emerging technologies that might fall under regulatory scrutiny.

Global Context: Comparing EU regulations with those in other jurisdictions, such as the U.S. or China.

10. Practical Activities and Assessments

Workshops: Building simple AI models and analyzing their compliance with EU laws.

Scenario Analysis: Addressing hypothetical situations involving AI misuse or ethical dilemmas.

Compliance Simulation: Conducting mock audits to ensure understanding of regulatory requirements.

Target Audience

The course content should be tailored to different stakeholders:

• General Public: Basic awareness of AI rights and responsibilities.

• Businesses and Developers: Practical tools for building compliant and ethical AI systems.

• Policymakers and Regulators: Deep understanding of regulatory frameworks and enforcement mechanisms.

If you would like us to deliver AI Literacy Training either online or on site please reach out to us at info@softwarestrategyconsulting.co.uk or fill out the contact form on the contact tab.