Towards Fairer AI: Strategies for Instance-Wise Unlearning Without Retraining

The increasing reliance on machine learning models in critical applications raises concerns about their susceptibility to manipulation and exploitation. Once trained on a dataset, these models often retain information indefinitely, making them vulnerable to privacy breaches, adversarial attacks, or unintended biases. Therefore, techniques are urgently needed to allow models to unlearn specific data subsets, reducing […]

The post Towards Fairer AI: Strategies for Instance-Wise Unlearning Without Retraining appeared first on MarkTechPost.

adversarial adversarial attacks ai paper summary ai shorts applications artificial intelligence attacks biases breaches concerns dataset editors pick exploitation information instance machine machine learning machine learning models making manipulation privacy raises reliance retraining staff strategies tech news technology them unlearn unlearning vulnerable wise