AI Legal Case Prediction Model
Budget: $1,500 – $3,000 USD
I am building an AI system that sits squarely at the crossroads of economics and law, yet its immediate purpose is very clear: help judges, attorneys, and policy teams anticipate case outcomes before they reach a courtroom. The core deliverable I need from you is a working predictive model that ingests structured and unstructured legal data, learns from historical rulings, and returns a probability score for how a new case is likely to be decided.
The focus is expressly on case-prediction rather than general document parsing. While the exact type of matter—criminal, civil, or corporate—can be finalized after we review data availability together, the engine you create must remain adaptable so it can be fine-tuned for any of those domains without major re-architecture.
Here is the workflow I have in mind:
• Data assembly & cleaning: Scrape or source publicly available dockets, rulings, and any relevant economic indicators; anonymize where necessary.
• Feature engineering: Combine legal factors (precedents, statutes cited, judge history) with macro-economic variables when they materially affect outcomes.
• Model development: Use a transparent approach—e.g., gradient-boosted trees or transformer-based NLP—so we can explain predictions to non-technical stakeholders.
• Evaluation: Provide precision, recall, and calibration metrics on a held-out test set, plus a short memo interpreting strengths and limits.
• Handoff: Deliver commented Python notebooks or scripts, a requirements.txt/conda file, and concise deployment notes.
I am comfortable with common open-source stacks—scikit-learn, XGBoost, PyTorch, spaCy—but if you have a proven library that better suits legal text, I am open to hearing why.
Acceptance criteria
1. Predictive accuracy meets or exceeds a baseline we agree on during kickoff.
2. Model explanations (feature importances or SHAP plots) convincingly outline why the AI reaches a given forecast.
3. All code runs end-to-end on fresh setup using the instructions you provide.
If this intersection of law, economics, and AI excites you, outline your proposed data sources, modeling approach, and prior work on similar decision-support tools when you respond.
The focus is expressly on case-prediction rather than general document parsing. While the exact type of matter—criminal, civil, or corporate—can be finalized after we review data availability together, the engine you create must remain adaptable so it can be fine-tuned for any of those domains without major re-architecture.
Here is the workflow I have in mind:
• Data assembly & cleaning: Scrape or source publicly available dockets, rulings, and any relevant economic indicators; anonymize where necessary.
• Feature engineering: Combine legal factors (precedents, statutes cited, judge history) with macro-economic variables when they materially affect outcomes.
• Model development: Use a transparent approach—e.g., gradient-boosted trees or transformer-based NLP—so we can explain predictions to non-technical stakeholders.
• Evaluation: Provide precision, recall, and calibration metrics on a held-out test set, plus a short memo interpreting strengths and limits.
• Handoff: Deliver commented Python notebooks or scripts, a requirements.txt/conda file, and concise deployment notes.
I am comfortable with common open-source stacks—scikit-learn, XGBoost, PyTorch, spaCy—but if you have a proven library that better suits legal text, I am open to hearing why.
Acceptance criteria
1. Predictive accuracy meets or exceeds a baseline we agree on during kickoff.
2. Model explanations (feature importances or SHAP plots) convincingly outline why the AI reaches a given forecast.
3. All code runs end-to-end on fresh setup using the instructions you provide.
If this intersection of law, economics, and AI excites you, outline your proposed data sources, modeling approach, and prior work on similar decision-support tools when you respond.