Sources of Error and Bias in AI Systems

Sources of Error and Bias in AI Systems

This course will equip you to master the identification and mitigation of systematic errors and algorithmic bias throughout the AI lifecycle. You will learn to recognise and diagnose various sources and types of errors and bias within AI models and data, and develop effective strategies for their mitigation, particularly in high-stakes applications. The programme explores how critical dataset selection and methodological choices profoundly impact the fairness and accuracy of UK data products, enabling you to evaluate and analyse these influences to ensure responsible AI development.