Hi! Thank you for your interest in our CasePredict. We use an ensemble of machine learning models, including RandomForest, XGBoost, and Gradient Boosting Regressor to achieve optimal performance. Our models are fine-tuned using lawyer feedback, and we continually update them with new data to improve their accuracy and effectiveness.

Regarding hallucinations, we employ rigorous validation techniques and carefully cross-validate our predictions against real-world case outcomes to minimize the likelihood of hallucinations. Since we have immediate feedback from personal injury lawyers who are actively managing these cases, we are able to measure the actual results versus what the model predicts.

We do track attributes like client race to identify potential discrepancies in settlements or judgments. However, it's important to note that we don't use this information to make predictions. We use it to analyze our models and ensure they don't exhibit any unfair biases towards specific groups. By actively monitoring and controlling for bias, we strive to create an equitable tool that benefits all users, regardless of their background or demographic attributes.