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AI bias occurs when a system produces results that are systematically prejudiced due to flawed assumptions in the training data or algorithm. It can reflect and amplify existing societal inequalities, leading to unfair outcomes for certain groups.
Biased AI can cause real harm — from discriminatory hiring decisions to unfair loan approvals — making bias detection and mitigation essential for any organisation deploying AI.
Amazon scrapped an internal AI recruiting tool after discovering it penalised CVs that included the word 'women's', because the training data was dominated by male applicants.
Explainability (XAI)
Explainability refers to the ability to understand and communicate how an AI system arrives at its decisions or predictions. An explainable model allows humans to inspect its reasoning, rather than treating it as an opaque 'black box'.
Responsible AI
Responsible AI is an approach to developing and deploying AI systems that prioritises fairness, transparency, accountability, and societal benefit. It encompasses practices and principles designed to minimise harm and ensure AI serves people equitably.
Training Data
Training data is the collection of examples used to teach a machine learning model. The model analyses this data to discover patterns and relationships, which it then uses to make predictions or generate outputs on new, unseen data.
Our programme follows a structured Level 4 curriculum with project-based learning, practical workflows, and guided implementation across business and career use cases. Funded route available for UK citizens and ILR holders.