The Value of Machine Unlearning

Our work as data scientists often focuses on building predictive models. We work with massive amounts of data, and the more we have, the better our models and forecasts can get. When we have a high-performance model, we keep retraining and iterating, introducing new data as required to keep our model fresh and free of deterioration. The result is that the performance level of the model is largely maintained, thus we continue to deliver value to users.

But what happens if constraints are imposed around a single data set or data point? How then do we remove this information without compromising the model in general and without initiating potentially intense retraining sessions? A potential answer that is gaining interest and that we’d like to explore is the abolition of machine learning.

unloading machine It is a Nascent field, with research and development providing some compelling results. It offers a potential solution to many of the problems industries face, from the costly rework needed in the face of new data laws and regulations to trying to detect and mitigate bias.

Dealing with unwanted data

For data science teams, bringing a high-performance model out of production due to legal or regulatory changes is not an uncommon problem. The process of retraining a large model, however, is extensive and expensive.

Take the example of the default lending approval form in the United States. Across states, we’ll likely have dozens to hundreds of data points, from which we’ve generated hundreds of features that we use to train a huge neural network. The time and cost that it would take to train this model, since we may be using very expensive hardware (such as multiple GPUs), can be great. Now imagine that this model has been in production for a year, delivering significant value to customers, when new California privacy laws are introduced that prohibit the use of a specific area of ​​the dataset.

We are now in a difficult situation, because the only option we have is to retrain our model. But What if there was a way to make the model forget this data without explicit retraining on the reduced dataset? This is basically what de-learning can do, which has huge benefits for organizations as well as individuals.

Privacy is a major concern for all of us. In financial services and other highly regulated industries, such as healthcare, breaching privacy laws can be a mission-critical problem, so seamlessly removing data that is no longer permitted by law provides a jail-free exit ticket. For an individual, especially one in Europe, their right to forget their right to the GDPR, an uncoordinated device could also be the means by which they maintain that right.

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Machine delivery can be another illegal way to provide value to both individuals and organizations is to remove biased data points that are identified after exemplary training. Despite laws prohibiting the use of sensitive data in decision-making algorithms, there are many ways bias can find its way through the back door, leading to unfair outcomes for minority groups and individuals. There are also similar risks in other industries, such as health care.

When a decision can mean the difference between life-altering and, in some cases, life-saving conflicts, algorithm hostility becomes a social responsibility and algorithms are often unfair because of the data they are trained on. For this reason, financial inclusion is an area that is rightfully the main focus of financial institutions, and not just for the sake of social responsibility. Challengers and FinTech companies continue to devise solutions that make financial services more accessible.

Protection from model degradation

From the perspective of model monitoring, machine learning depreciation can also protect against model degradation. Models that have been in production for a long time will contain data that becomes less important over time. A prime example of this is the way customer behavior has changed after the pandemic. In banking, for example, customers quickly transitioned to digital channels where they opted for in-person interactions once they made a choice. This behavioral paradigm shift made it necessary to retrain many paradigms.

Another use case could remove data that might lead to a narcotic attack, or ramp up treatment when bad data is presented, for example, a system failure that causes a model to deliver malicious results. Again, the primary motivation for this use case is to reduce rework, but also to make models and data science in general more secure.

how to start

Researchers working on how to introduce machine learning declutter have proposed a framework called Segmented, Isolated, Segmented, and Aggregated (SISA) training. This approach divides the training data into subsets called segments, which are essentially smaller models that make up the larger model. If there is a need to remove the data inside these parts, only these parts need to be retrained, which can happen separately. Retraining is still needed in small portions with SISA, but alternative research around forests that has been supporting data removal (DARE) that raises caching in nodes in an effort to forget and the need for any clear retraining.

This promises the data science community and the companies that models provide a large part of the business value, but there is potential for the need to remove data in a dynamic and changing environment.

It’s a fundamental question for the data science community, which is why we wanted to discuss the areas where we see the lack of machine learning offering the most value.

Now that you have our thoughts, we’d love to hear your thoughts. Please leave a comment below, or contact us, and let’s continue this wonderful and lively conversation


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