AI tools typically work from limited inputs and generic averages. In real Culver City cases, the facts usually hinge on details such as:
- Intersection and crosswalk conditions (visibility, signal timing, turning behavior)
- Pedestrian/bicycle presence near high-traffic corridors and event areas
- Driver distraction or speeding claims based on witness and electronic data
- Multiple possible causes (impact sequence, medical progression, pre-existing conditions)
When liability is disputed—or when causation is complex—an AI model can’t weigh credibility the way insurance adjusters and juries do.


