Transfer Learning
A technique where a model developed for one task is adapted for a related task, reducing development time but requiring governance of inherited biases.
Involves fine-tuning pre-trained models on new, task-specific data. While efficient, transfer learning carries forward any biases or security weaknesses from the base model. Governance requires evaluating both base and fine-tuned model biases, tracking base-model provenance and licensing, and documenting fine-tuning datasets and hyperparameters to ensure legal and ethical compliance.
A chatbot team fine-tunes an open-source BERT model on customer-service transcripts. Governance mandates a bias audit comparing sentiment across customer demographics on the base and fine-tuned models. They document the base-model license to confirm commercial use rights and log all fine-tuning parameters for reproducibility.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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