Transform Knowledge with Causal Analysis
Explore our multi-stage framework for neuron identification and causal intervention in AI models.
Innovative Causal Analysis Framework
We specialize in multi-stage causal analysis, focusing on neuron identification, causal intervention, and attribution validation to enhance knowledge accuracy in AI systems.
Causal Intervention
Test factual degradation by selectively deactivating high-impact neurons and observing knowledge drift.
Causal Intervention
Deactivate high-impact neurons to test factual degradation effects.
Attribution Validation
Compare methods for consistency in identifying knowledge-critical neurons.
Advancingmodelinterpretabilityandreliability:
DebuggingFactualErrors:Amethodologytotraceinaccuraciestospecificmodel
components.
ControlledKnowledgeUpdates:Enabletargetededitingoffactualknowledgewithoutfull
retraining.
Bias&MisinformationMitigation:Identifyneuronscontributingtoharmfulfactual
associations(e.g.,stereotypes).