Few-Shot Learning

Few-shot learning is a machine learning approach where models are trained with only a small amount of data instead of requiring massive datasets. It enables AI systems to quickly adapt to new tasks with minimal examples, reducing the need for extensive data collection.

This method is significant for AI ethics and human rights because it can minimize potential biases that arise when large amounts of training data are used without proper oversight. By relying on fewer examples, few-shot learning encourages more careful and deliberate curation of data, which is essential for ensuring fairness and accountability in automated systems. It also opens discussions on how AI technologies should be developed and regulated to protect individual rights in data-scarce environments.

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