What is Federated Learning
One of the hardest parts of building a good AI or ML model is having the right amount of data to make good predictions from.
Claudius has gathered over information from 50,000+ cases. However, acting strictly on historical data limits your model's ability to evolve over time. At the same time, attorney-client privilege limits in the influx of new data.
Under Naive ML practices, Claudius would have to maintain separate models for each customer to ensure case details don't flow between customers.
However, using federated learning keeps all these "clusters" of small data private, while building a robust global model.
In machine learning, data is transformed from its raw form into "features" and then the model calculates "weights" that it applies to these features to make a decision. Under federated learning, those "weights" are shared with a global model which gives the best possible answer considering all data sources.
This approach protects the consumer's data and still allows the model to continuously get smarter.
Claudius.ai has strong product sense about this industry. Since Joseph holds a J.D., he empathizes with the struggles of personal injury attorneys and is able to design a solution most fitting to them with his team. This yielded two unique moves, securing product strength.
The integration of predictive analytics with attorneys' workflows helps to drive sustained product adoption and stickiness in a world where attorneys are too quick to abandon virtual folders for manila folders.
Their grand vision for the company still includes reinventing legal intelligence by introducing the ability to eliminate bias from our legal system and accelerate litigant with case automation technologies. If you're curious to learn more and get involved, reach out to email@example.com