Not all board members are equipped with in-depth knowledge of the technical details of AI, yet they need a foundation upon which decisions about AI in their organisations are made.
This workshop covers basic types of machine learning models such as supervised learning, e.g., linear regression, decision trees, un-supervised learning, e.g., clustering, dimensionality reduction and reinforcement learning.
Further, the purpose of the model: – what are the different models designed to achieve, i.e. prediction, classification or clustering – together with model interpretability.
What performance indicators are used to evaluate the model and the reasons, why this is relevant.
Data requirements and quality as different models require different types and different amounts of data.
Ethics and compliance: – some key aspects to consider to ensure the models comply with relevant regulations and ethical guidelines, especially when dealing with sensitive data.
Maintenance, calibration and updates – considerations re re-training and updating as new or more extensive data becomes available.
