🔍 AI for Privacy-By-Design & Trust-Centered Data Practices

Build a practical understanding of how privacy and trust can be embedded into AI systems from the outset. This course explores responsible data use, clear information flows, meaningful user control and safeguards that remain effective as AI capabilities change.

Across eight lessons, you will examine data minimisation, transparency, explainability, fairness and privacy risk assessment. Practical examples explore AI assistants, retrieval systems, persistent memory and agents that take actions, showing how design choices affect people and their information.

You will also consider how AI can assist with mapping and reviewing systems, where human judgement remains essential, and what emerging privacy technologies can realistically offer. The emphasis is on testing safeguards, evaluating evidence and maintaining accountability throughout a system’s lifecycle.

Designed for professionals, managers, designers and anyone involved in adopting AI, this course provides an accessible foundation for making informed decisions about privacy and trust. It focuses on practical design principles rather than jurisdiction specific legal requirements, with no programming knowledge required.