Responsible & Ethical AIin Leeds

Module 1: Introduction to Responsible & Ethical AI

• Fundamentals of Artificial Intelligence and ethical AI principles
  • Definition and importance of responsible AI
  • Benefits and challenges of AI adoption
  • Ethical considerations across AI applications

Module 2: Core Principles of Ethical AI

• Understanding foundational ethical principles
  • Fairness, accountability, transparency, and human oversight
  • Respect for human rights and individual autonomy
  • Reliability, safety, and responsible innovation

Module 3: AI Bias and Fairness Assessment

• Identifying bias in AI systems
  • Sources of bias in datasets and algorithms
  • Fairness evaluation concepts and approaches
  • Strategies for reducing unfair outcomes

Module 4: Data Privacy and Responsible Data Usage

• Managing data responsibly throughout the AI lifecycle
  • Personal data protection and consent
  • Data minimisation and purpose limitation
  • Confidentiality, access controls, and secure data handling

Module 5: Transparency and Explainable AI

• Improving the understandability of AI systems
  • Explainability and interpretability fundamentals
  • Communicating AI capabilities and limitations
  • Documenting AI-generated decisions and outputs

Module 6: AI Accountability and Human Oversight

• Establishing responsibility for AI-assisted decisions
  • Human-in-the-loop and human-in-command approaches
  • Defining roles and responsibilities
  • Reviewing AI recommendations before consequential actions

Module 7: Responsible Generative AI and LLMs

• Applying ethical practices to generative AI applications
  • Hallucinations, misinformation, and harmful outputs
  • Prompt safety and responsible content generation
  • Copyright, attribution, and intellectual property considerations

Module 8: AI Risk Identification and Impact Assessment

• Evaluating potential risks associated with AI systems
  • Identifying technical, ethical, and operational risks
  • Assessing potential impacts on individuals and communities
  • Developing risk mitigation and escalation plans

Module 9: AI Governance and Regulatory Awareness

• Understanding governance practices for responsible AI
  • AI policies, standards, and organisational controls
  • Overview of relevant AI regulations and guidance
  • Governance responsibilities and compliance documentation

Module 10: AI Security, Safety, and Robustness

• Supporting safe and dependable AI operations
  • Adversarial risks and misuse scenarios
  • Model reliability and robustness considerations
  • Secure AI development and deployment practices

Module 11: Responsible AI Testing and Evaluation

• Evaluating AI systems against ethical and operational requirements
  • Testing for bias, reliability, and harmful outputs
  • Defining evaluation criteria and acceptance thresholds
  • Recording findings and recommending improvements

Module 12: Ethical AI in Business and Industry

• Applying responsible AI practices across business functions
  • Ethical considerations in healthcare, finance, education, and recruitment
  • Stakeholder impact and sector-specific risks
  • Practical examples of responsible AI decision-making

Module 13: Responsible AI Monitoring and Continuous Improvement

• Maintaining responsible AI practices after deployment
  • Monitoring model behaviour and emerging risks
  • Managing user feedback and AI-related incidents
  • Reviewing controls and updating governance procedures

Module 14: Capstone Project – Responsible AI Assessment

• Apply course concepts to an illustrative AI use case
  • Define the AI use case, stakeholders, and intended outcomes
  • Conduct an ethical risk and impact assessment
  • Prepare a responsible AI checklist and mitigation plan
  • Present findings, limitations, and recommendations