The idea that personal data belongs to individuals is a key principle in laws like the European Union’s General Data Protection Regulation (GDPR). A critical part of GDPR is the “right to be forgotten,” which allows people to request the deletion of their personal information from a company’s records. While this seems straightforward, it becomes much more complicated when artificial intelligence (AI) is involved, AI Neural Networks.
AI models, particularly those built using neural networks, rely on vast amounts of data to learn and perform tasks. When your data is used to train these models, removing it isn’t as simple as hitting a “delete” button. Erasing information from a neural network is complex, much like performing surgery on a brain. So, how can we teach AI to forget specific data?
The Ethics of Forgetting in AI
The “right to be forgotten” is not just about legal rules; it’s also an ethical issue in today’s digital world. Using personal data to train AI models is becoming a major concern, and it falls into a gray area when it comes to laws.
For example, The New York Times recently sued OpenAI after finding out that ChatGPT could quote its articles without giving credit. Cases like this could set important guidelines for how AI models handle personal and business-related data in the future.
Besides the legal side, there’s the problem of AI models becoming too large and complicated. Some models, like GPT-3, are trained on massive datasets, making it hard to know exactly what data they’ve learned. This could lead to the accidental use of false information, personal data, or biased content. Deleting this data from AI models is challenging, and retraining them from scratch is expensive and time-consuming.
How AI Learns—and Why Forgetting Is Hard
To understand why AI has a tough time forgetting, we need to know how AI learns. AI neural networks are designed like a human brain, where neurons connect to each other. When an AI is trained on data, like pictures of cats and dogs, it updates its “neuron connections” (also called weights) based on that data.
Once the AI has learned something, it becomes part of those connections. Trying to forget a specific piece of data without disrupting everything else is tricky—just like removing one memory from a brain without erasing other memories.
Ways AI Can Forget Data
Researchers are working on different ways to help AI forget specific data without losing everything else it has learned. Here are some of the methods:
Retraining on Remaining Data
One solution is to retrain the AI model on the data that doesn’t need to be forgotten. This method gradually overwrites the unwanted information, but it’s costly and takes a lot of time.Reversing the Learning Process
Another method involves reversing the learning process. The idea is to use the data that needs to be forgotten to “undo” the training. However, this method is still not reliable, and it’s hard to guarantee that all the data has been removed.Gradual Data Introduction
A third method is redesigning the way AI is trained. By introducing data gradually, it becomes easier to roll back to a point before the unwanted data was learned. However, this solution only works if the data in question was introduced late in the training process.
The Future of AI Forgetting
Teaching AI to forget is still a new field, but companies like Google and JPMorgan Chase are exploring ways to do it. As AI becomes more popular and powerful, the need for ethical AI practices will grow. This includes finding better ways to erase data from AI systems.
Making sure AI can forget is essential not only for following privacy laws like the GDPR but also for building public trust in AI. While no perfect solutions exist yet, the future looks promising as researchers continue to develop new techniques to balance AI’s capabilities with the “right to be forgotten.”
