Description
Building Trust and Transparency in Intelligent Systems
As artificial intelligence becomes increasingly integrated into decision-making processes across industries, the need for transparency and accountability in these systems has never been more critical. This course explores the principles and practices of Explainable AI (XAI)—a vital field that seeks to make AI decisions understandable and interpretable by humans.
Participants will gain insight into the challenges posed by so-called “black-box” models, where complex algorithms produce outcomes that are difficult to trace or justify. Through practical examples and real-world case studies, learners will examine the risks of opacity, including potential impacts on fairness, regulatory compliance, and public trust.
By the end of the course, participants will be equipped with the knowledge to assess the value of explainability in AI, communicate model behaviour to stakeholders, and support responsible AI adoption within their organisations.
This course is ideal for professionals working in data science, compliance, risk management, AI development, or any role where ethical and transparent AI deployment is essential.