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a blog explaining why is machine unlearning really required https://www.wired.com/story/machines-can-learn-can-they-unlearn/ |
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this blog here summarizes all the stuff about machine unlearning and its important in the field of AI importance of the machine unlearning and the possible contributions from our side; |
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Machine Unlearning Project ScopeObjectives and scope of Machine Unlearning project. A Machine Unlearning project aims to develop techniques and methodologies for a machine learning model to selectively forget or update previously learned information. The objectives and scope of such a project typically include: Selective Forgetting: Design algorithms to allow a model to forget specific information while retaining relevant knowledge. This could be achieved through various techniques such as regularization, retraining, or neural architecture modifications. Dynamic Adaptation: Enable the model to adapt to changing data distributions or priorities over time. This might involve techniques like continual learning or transfer learning with an emphasis on forgetting outdated information. Resource Efficiency: Optimize the memory and computational resources required for unlearning to ensure it can be practically implemented in real-world scenarios, particularly in resource-constrained environments. Preservation of Core Knowledge: Ensure that fundamental and critical knowledge is retained, even as less relevant or outdated information is forgotten. This requires defining and quantifying what constitutes "core knowledge." Ethical Considerations: Address ethical concerns, such as ensuring that the unlearning process doesn't lead to biased or discriminatory behavior, and establishing mechanisms for transparent and accountable decision-making. Evaluation Metrics: Develop metrics and benchmarks to assess the effectiveness of unlearning techniques, considering factors like retention of core knowledge, adaptability to new data, and resource efficiency. Applications: Identify specific domains or applications where machine unlearning is particularly valuable. This might include scenarios with rapidly changing environments, evolving user preferences, or sensitive data privacy concerns. Security and Privacy: Investigate potential vulnerabilities introduced by unlearning processes, and design safeguards to prevent unauthorized or malicious attempts to manipulate the model's knowledge. User Interaction: Explore interfaces or mechanisms through which users or administrators can guide the unlearning process, providing feedback or constraints on what information should be forgotten. Benchmark Datasets and Environments: Create standardized datasets and environments for testing and benchmarking machine unlearning techniques, ensuring reproducibility and comparability across different research efforts. Real-world Deployment: Consider practical challenges in deploying machine unlearning in production systems, including issues related to scalability, adaptability to specific tasks, and integration with existing machine learning workflows. |
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MethodologiesToday, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in artificial intelligence, and especially machine learning (ML), its existence can be a threat to user privacy, and it can weaken the bonds of trust between humans and AI. Recent regulations now require that, on request, private information about a user must be removed from both computer systems and from ML models, i.e. ``the right to be forgotten''). While removing data from back-end databases should be straightforward, it is not sufficient in the AI context as ML models often `remember' the old data. Contemporary adversarial attacks on trained models have proven that we can learn whether an instance or an attribute belonged to the training data. This phenomenon calls for a new paradigm, namely machine unlearning, to make ML models forget about particular data. It turns out that recent works on machine unlearning have not been able to completely solve the problem due to the lack of common frameworks and resources. Therefore, this paper aspires to present a comprehensive examination of machine unlearning's concepts, scenarios, methods, and applications. Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications. In addition, we aim to highlight the key findings, current trends, and new research areas that have not yet featured the use of machine unlearning but could benefit greatly from it. We hope this survey serves as a valuable resource for ML researchers and those seeking to innovate privacy technologies |
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Testing technologics used In machine unlearningIn machine unlearning projects, various testing technologies and methodologies are employed to evaluate the effectiveness and performance of the unlearning techniques. Some of these testing technologics include: Test Datasets: Curated datasets with known characteristics are used to assess the model's ability to forget specific information while retaining relevant knowledge. These datasets may include scenarios where information becomes outdated or less relevant over time. Continual Learning Benchmarks: Benchmark environments and datasets specifically designed for continual learning scenarios, which are relevant for machine unlearning projects. These benchmarks help evaluate how well a model adapts to changing data distributions. Metrics for Core Knowledge Retention: Metrics that quantify the model's ability to retain core or critical knowledge while unlearning less relevant information. These metrics help assess the trade-off between forgetting and retaining important knowledge. Transfer Learning Evaluations: Techniques for evaluating the transferability of knowledge from the pre-unlearning state to post-unlearning tasks. This may involve fine-tuning on new tasks to assess the impact of unlearning. Adversarial Testing: Evaluating the model's resistance to attempts to maliciously manipulate the unlearning process, ensuring that unauthorized parties can't exploit it for nefarious purposes. Dynamic Dataset Simulations: Simulating scenarios where the data distribution changes over time, allowing researchers to evaluate the model's adaptability and forgetting capabilities in dynamic environments. Resource Utilization Metrics: Assessing the memory and computational resources required for unlearning compared to traditional training processes. This helps ensure that unlearning techniques are feasible for practical deployment. User Feedback and Evaluation: Incorporating user feedback to validate the effectiveness of the unlearning process. This can involve surveys, user studies, or interactive interfaces for users to guide the unlearning process. Ethical and Fairness Evaluations: Testing for biases or fairness concerns introduced by unlearning processes, ensuring that they do not disproportionately affect specific groups or demographics. Model Robustness Testing: Assessing the model's robustness to perturbations and adversarial attacks, particularly in scenarios where unlearning may introduce new vulnerabilities. Privacy Audits: Evaluating the privacy implications of unlearning, ensuring that sensitive information is properly handled and protected during the unlearning process. |
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Summarize all your discoveries on the concepts here
@Chiragkj @veerasagar
link for the reference:
https://arxiv.org/abs/2209.02299
https://unlearning-challenge.github.io/
the github repo link for the research in the above paper:
https://github.com/tamlhp/awesome-machine-unlearning
the abstract for the project and other such things can be summarized from this research article:
https://www.kaggle.com/code/tamlhp/machine-unlearning-the-right-to-be-forgotten
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