Multimodal Fusion Fairness
R4 evaluates bias where modalities combine inside a model. Fixed evaluation conditions support a comparison between actual fusion and a baseline that disables registered cross-modal interactions in the same computation graph, exposing amplification and protective cancellation.
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Multimodal Fusion Fairness / KEY RESPONSIBILITIES01 02 03
Correct locally
Adopt or restore
Trace harmful fusion effects to their source and test local corrections against every required evaluation cell.
HOW IT WORKSA clear process.
A clear process.
Traceable decisions.
Diagnose the source
Preserve the direction of amplification and cancellation, then use evidence collected before correction to locate the harmful source.
Correct locally
Modify the validated internal fusion location while preserving protective cancellation, then rerun under the same evaluation conditions.
Adopt or restore
Check raw and adjusted scores in every required modality–fairness cell, alongside performance, latency and group effects; missing evidence holds the decision and any failed cell prevents adoption. Adopt only a qualifying candidate or restore the checkpoint, isolating incomplete restoration.
APPLICATIONSWhere the architecture
Where the architecture
can make a difference.
- Evaluation and internal correction of multimodal models combining different input representations.
R4’s correction evidence grants no authority to execute, permit or cancel an external action.
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