Foundation Model

A foundation model is a large AI model trained on broad, general-purpose data — typically at massive scale using self-supervised or unsupervised learning — that can be adapted (fine-tuned) for a wide range of downstream tasks. Examples include CLIP, DinoV2, GPT-4, and BLIP. Foundation models are significant for accessibility because they lower the barrier to building AI-powered assistive tools: a single pre-trained model can be fine-tuned with relatively small domain-specific datasets to produce image classifiers, caption generators, or speech recognisers. However, they also inherit biases from their training data and may perform unevenly across different demographic groups or content types.