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Meet the course instructors and fellow participants

This module is a comprehensive introduction to AI fundamentals, covering machine learning types, data handling, prediction methods and model evaluation metrics.

• Understanding AI: A gentle introduction to how machines learn from data
• Types of Machine Learning: supervised (from examples), reinforcement (through trial and rewards) and unsupervised learning (on its own)
• Working with Data: Representing data, Categorical vs real valued attributes, features
• Making Predictions: Brief overview of Bayesian methods, logistic regression, nearest neighbours, decision trees, neural networks
• Measuring Success of models: Accuracy, precision and recall

This module covers deep learning and generative AI fundamentals, from neural networks to practical applications involving text and image generation, along with future vision.

• Understanding Deep Learning: Neural networks, layers, and how they process information
• Large Language Models: Architecture, training, and capabilities of transformer-based models
• Text Generation: Prompt engineering, text completion, and creative writing applications
• Image Generation: Text-to-image models, style transfer, and visual content creation
• Practical Applications: Using ChatGPT, DALL-E, and other generative AI tools in business contexts
• Generative AI vision for the future

This module covers the fundamentals of financial services' legal and regulatory frameworks, the problem of bias, along with essential data management principles.

• An overview of the existing UK legal and regulatory requirements to comply with when developing, procuring, deploying, and using AI.
• Comparisons with approaches in other jurisdictions, including the EU and US.
• An overview of key relevant international governance and technical standards.
• A consideration of current and forthcoming regulatory developments.
• Review of key data risks, and consumer duty.
• Understanding the implications of using proprietary data in the context of AI.
• Review of legal and regulatory requirements and guidance.

This module covers the accuracy and validity of machine learning models, along with issues such as transparency, bias and explainability.

• Review of key risks relating to reliability of AI systems, statistical accuracy, data accuracy, and the validity of data inputs and generated outputs.
• Examples of hallucinations, reliability metrics, and benchmarks.
• Understanding transparency: A deep dive into transparency in context (audiences as clients, financial consumers, regulators, suppliers, and other stakeholders).
• Best practice approaches to explainability and a consideration of explainability frameworks.
• Understanding bias risks and concepts.
• Consideration of the differences between bias, discrimination, unintended outcomes, and consequences for vulnerable customers.

This module covers IP and oversight aspects of AI systems, including standards, monitoring, disclosure requirements, risk controls, and intellectual property considerations including ownership and licensing.

• Relevance and state of standards relating to bias and other risk controls.
• Understanding monitoring and oversight throughout the AI lifecycle and expectations at each stage.
• A consideration of expectations forming around disclosure of confidential information relating to the development and use of AI systems.
• Regulatory disclosure requirements in the context of AI.
• Intellectual property considerations:
  • Ownership and infringement issues
  • Licensing approaches
  • Recent AI case law and its impact on IP protection across jurisdictions

This module covers key aspects of AI governance and risk management:

• Understanding AI harms, fault attribution, and consequences
• Risk transfer considerations in AI procurement and emerging frameworks
• Senior Managers and Certification Regime (SM&CR) implications and governance structures
• Organizational approaches to AI governance:
  • Internal stakeholder involvement and timing
  • Board reporting on AI risks
• Implementation of AI ethics frameworks in Financial Services