Influence becomes traceable
Every block represents a training shard or learned region. A request sends a forensic scan through the field to reveal where the selected influence still propagates.
REVOKE
Consent should survive training.
A fictional machine-unlearning system that traces and removes the influence of personal data, poisoned sources, or unwanted behavior from trained models, then produces an auditable revocation receipt.
Enter live frameThe premise
AI consent is usually treated as a decision made before training. REVOKE asks what happens when that decision remains active afterward: an influence can be located, isolated, removed, verified, and attached to a durable model-version receipt without pretending the model itself lives onchain.
Three connected systems
PREMISE / INTERFACE / BEHAVIORRemoval stays selective
Personal data, poisoned sources, and model behavior produce different influence shapes. The affected region collapses while the surrounding capability field visibly rebalances.
Verification becomes a receipt
The simulation closes with a residual estimate, model-version fingerprint, and fictional Solana attestation that records proof metadata rather than model weights or private data.
The build
Product thinking,
made interactive.
- 01Original fictional machine-unlearning product identity
- 02Dependency-free Canvas 2D influence-field engine
- 03Personal Data, Poisoned Source, and Model Behavior request modes
- 04Locate, Isolate, Unlearn, Verify, and Attest lifecycle
- 05Interactive pointer, touch, and keyboard inspection
- 06Canvas fallback, fullscreen, reduced-motion, and focus-safe protocol panel
SIMULATION NOTICE
REVOKE is a fictional product presented as an interactive frame. Every model, shard, influence score, residual estimate, request, proof, timing, and attestation is simulated. It does not access a trained model, remove real data, verify machine unlearning, connect to Solana, access a wallet, or initiate a transaction.
Production path
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