Choose which data to make your model forget (Unlearn!), but watch out - every deletion ripples!
Machine unlearning is the process of making a trained AI model selectively forget specific data points while maintaining its performance on other data. Think of it as surgical removal of information from the model's memory.
When you make a model forget one piece of data, it affects how the model performs on similar data points. This creates a "ripple effect" across different demographic groups. For example, making the model forget data about one age group might affect its predictions for other age groups.
Choose data points to unlearn that will have minimal negative impact on the model's overall fairness. Try to maintain balanced performance across all demographic groups while successfully removing the targeted information.