Posts on what we actually think.
Essays on conformal prediction, social inflation, and where generative AI helps and where it doesn't. Written by the team.
Showing 14 posts · David H. Silver
Clear filtersWhy Traceability Beats Accuracy Alone
A model that spits out a perfect prediction with zero explanation is a liability in a high-stakes claim. Trust requires knowing exactly which medical record or pleading drove the math.
Geometric Machine Learning on Resolved Cases
Large language models are word guessers, not calculators. To predict the financial outcome of a lawsuit, you must separate the extraction of text from the mathematics of risk.
Measuring Calibration: Why We Publish Error Rates
A claim prediction is useless if you do not know how often the model is wrong. Publishing error rates forces a transition from guessing to actual risk management.
Neural-Symbolic Models for Legal Outcome Prediction
Generative AI is a text engine, not a crystal ball. To forecast litigation outcomes accurately, you must fundamentally separate the act of reading a claim file from the mathematics of predicting its cost.
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