Best AI tools for< Unlearn Sensitive Information >
2 - AI tool Sites

The Unlearn Platform
The Unlearn Platform is an AI-powered platform that streamlines clinical trials by creating digital twins of patients using clinical trial data. It predicts clinical outcomes with unparalleled precision, accelerates timelines, enhances decision-making, and identifies sensitive clinical outcomes. The platform minimizes trial failure, cuts costs, and accelerates approval, ultimately optimizing study endpoints and composite scores.

Up Learn
Up Learn is an AI-powered educational platform designed to help students improve their A Level grades with a guarantee of achieving A*/A results or a money-back offer. The platform offers interactive videos covering the entire curriculum, detailed progress tracking, personalized revision based on AI algorithms, exam-board specific courses, guided exam practice, and 24/7 tutor support. Up Learn aims to make learning simple and efficient, ensuring that every second of study counts towards exam success.
5 - Open Source AI Tools

awesome-llm-unlearning
This repository tracks the latest research on machine unlearning in large language models (LLMs). It offers a comprehensive list of papers, datasets, and resources relevant to the topic.

Awesome-GenAI-Unlearning
This repository is a collection of papers on Generative AI Machine Unlearning, categorized based on modality and applications. It includes datasets, benchmarks, and surveys related to unlearning scenarios in generative AI. The repository aims to provide a comprehensive overview of research in the field of machine unlearning for generative models.

open-unlearning
OpenUnlearning is an easily extensible framework that unifies LLM unlearning evaluation benchmarks. It provides efficient implementations of TOFU and MUSE unlearning benchmarks, supporting 5 unlearning methods, 3+ datasets, 6+ evaluation metrics, and 7+ LLMs. Users can easily extend the framework to incorporate more variants, collaborate by adding new benchmarks, unlearning methods, datasets, and evaluation metrics, and drive progress in the field.