Machine unlearning – a new frontier
Since there is so much talk about machine learning and ways it could be utilized, it is time to mention the exact opposite of it. We have mentioned so many times that the ML model is only as good as the data it was trained on. But what happens when that data is no longer of service to us? The issue arises when the model is already trained on it, and now you have to exclude it or revert the process. From machine learning, we turn to machine unlearning. A bit of a black box still, machine unlearning is gaining track. There are some scientific articles on it, but practical applications are almost still in its wraps. Especially in large language models (LLMs), it could be tricky to make the model, sort of, forget some parts of the data set it was already trained on. A theoretical approach to machine unlearning Machine unlearning is an emerging subfield of machine learning whose sole goal is to remove or forget specific subsets of data that the model was trained on or influenced by. It’s the process of unlearning or forgetting certain data points to retrain the model without that specific data or information. This derives from data privacy laws and “The right to be forgotten”. Private data and data whose security could be in jeopardy are something we definitely don’t want in some ML or AI models. However, once the model has been trained on data sets (training data) it is fairly difficult to revert the process without scraping the whole process which is extremely costly and time-consuming. Machine unlearning could be defined as the process of not only removing data and knowledge but also updating or adapting the trained model to ensure compliance with privacy and security and a higher level of accuracy. Let’s not forget that this approach is not something we can completely be sure about since the outcome of removing data could sometimes lead to model corruption and breakage. If you remove certain data points from the model, how can you be sure it wasn’t something integral that made the model work in high accuracy? Known ways to machine unlearning Despite being such an interesting concept, machine unlearning still requires more advancements. On a smaller scale, ML models can be reverted by retraining. Yes, it will prolong the time to get expected results from machine learning, but you will be sure that you’ve removed unwanted data sets. But, on the other hand, with larger models, retraining is far more expensive and requires more time and effort to do so. Retraining deep learning models or LLMs has those issues with cost and time. Machine unlearning can be grouped into two main categories: exact unlearning and approximate unlearning methods. Exact unlearning is done by removing certain data points (those we want the model to forget) by retraining the model from scratch without the data we want to exclude from the training set. As mentioned above, this is expensive. It’s also not sustainable, since it’s more likely that you will have to remove data points more than once. Forgetting data will be a recurring problem in large datasets so it’s not optimal to retrain the model every time this happens. Approximate unlearning methods try to approximate data points that would be included in the training set as if the model was trained on it without the data it has to unlearn. Often it is done by introducing new data points to replace or overwrite those we want to be forgotten. Or it finds outliers to be excluded from data sets. Various methods are being tested to see which ones will efficiently perform machine unlearning, but it still remains to see which ones are going to be fully efficient and reliable. Machine unlearning is still going through examination and experiments and there is no definite approach to the best practices. Why and where will it appear? Privacy laws, copyright issues, and data poisoning are only some of the factors that influence the development of machine unlearning and the evergrowing need for it. The need to remove unwanted or outdated information is driving advancements in unlearning. Often, data can be also labeled incorrectly or it encounters errors, faces adversarial attacks, or is manipulated somehow. In all these cases you would want to remove such data from the model at any cost. But you have to understand data first, before trying your hand at any machine unlearning method. It has to be known how a certain data point influences the model. Will the data you remove influence the model’s accuracy and its results? What is the reasoning behind forgetting data and will this set back your model? Time moves on and data changes Data and its value change over time. Something actual before, might not be now. For the ML or AI model’s accuracy, data needs to stay fresh and up to date. As much as anything else, data evolves as well. Better control over our private data “The right to be forgotten”, privacy laws, and legislation, all give private individuals to take control over their data. This in turn influences which part of our data can be used in ML and AI models. And it’s not only about our private information but also about content, art, and intellectual property that original creators didn’t allow those models to use. Removing bias Unfortunately, in ML and AI models there is bias. Depending on the data ingested, it can create unfair and biased results. Models can learn that some attributes are better than others and thus create inaccurate results that can lead to discrimination, manipulated results, or prejudice. By unlearning and removing data that produced bias, models can show more leveled results. Data resource optimization Ingesting vast amounts of data will make the models a bit more inflexible. Too much invaluable data can strain memory resources. Not all data points are valuable for models’ efficiency and accuracy. Models should prioritize essential information. With machine unlearning,