AI Litigation Index

📚 How We Calculate Risk

The AI Litigation Index predicts which AI products could cause companies to face big lawsuits. We look at three things:

• How often the AI makes mistakes — based on public tests like LMSYS Arena and HuggingFace.
• How bad the mistake would be — healthcare mistakes are much worse than entertainment mistakes, so we adjust the risk.
• How easy it is to win a lawsuit — based on real court data (like Stanford SCAC and Cornerstone Research).

Simple Formula:
Mistake Risk × Damage Multiplier × Lawsuit Win Chance = Final Risk

We combine these to forecast the financial risk each company faces if their AI product fails.

🗂️ What Each Column Means

• Company: The company that launched the AI product.
• AI Product: The name of the AI tool or service.
• Launch Date: When the AI was publicly launched or announced.
• Model: The AI engine behind it (like GPT-4, Claude 3, Llama 3).
• Wrong Answer Risk %: How often the AI gives wrong or misleading answers.
• Real Harm Risk %: How likely it is that a mistake actually causes real damage.
• Class Action Success %: How often people win class action lawsuits in that industry.
• Estimated Exposure ($): The possible financial loss from lawsuits.

🧠 Serious AI Needs Structure, Not Just Language Tricks

LLMs are good at generating text. They’re bad at delivering guarantees.

In finance, healthcare, insurance, and defense, guesses aren't good enough. Regulated industries don't tolerate hallucinations.

The next generation of serious AI will combine large models with neuro-symbolic reasoning, category theory, and formal knowledge graphs — because that's what surviving in regulated markets demands.

If you're building for where the world is going — not where it was last year — let's talk.