In 2014, we published research demonstrating that machine learning models could predict the outcomes of U.S. Supreme Court cases with meaningful accuracy. The work, which appeared in PLOS ONE and was subsequently covered by major media outlets, represented one of the earliest applications of AI to legal outcomes forecasting.
The approach was deceptively simple: by training models on historical case features — the issue area, the lower court decision, the ideological composition of the Court, oral argument characteristics — we could predict case outcomes at rates significantly above baseline. The model's performance on out-of-sample data provided strong evidence that the patterns were real, not artifacts of overfitting.
What made the research significant wasn't just the accuracy numbers — it was the demonstration that legal outcomes, often perceived as the product of pure reasoned judgment, contained statistical regularities that machines could detect. This finding had implications for legal strategy, judicial analytics, and the broader question of how law operates in practice.
The Supreme Court prediction work also illustrated a theme that would define much of our subsequent research and advisory practice: the intersection of AI capabilities with professional domains. The same questions we asked about judicial prediction — How accurate can AI be? Where does it fail? What are the ethical implications? — later resurfaced in our work on AI bar exam performance and CPA evaluation.
For Bommarito Consulting, this research established a foundation of credibility that continues to differentiate our advisory practice. When we advise organizations on AI capabilities and limitations, we do so as researchers who have published in the field, not merely as consultants who read about it.