AI Data Provenance & Theft Accusation Mapper Forensic Ledger

Grounded in Gizmodo tech investigation: "In the Wild West of AI, Everybody Is Accusing Everybody Else of Theft"

Total Tokens Scraped 14.2B Cumulative web corpus ingested

Infringement Risk Score High (78/100) Statutory exposure & liability index

Estimated Weight Attribution 4.1% Token retention in dense layer matrices

Defensible Fair Use Ratio 32% Transformative market-factor score

Interactive Ingestion Lineage & Contamination Graph

D3 Force Physics Active

Web Scrape

Processing

Model Weights

Infringement Claim

Forensic Parameter Matrix

VERIFIED AUDIT

Primary Target Dataset

Model Architecture Scale

Scraping Extraction Depth

Filter Scrub Sensitivity 0.75 Higher sensitivity removes copyrighted tokens at cost of language perplexity.

Active Accusations & Statutory Allegations

Unauthorized Scraping

CFAA § 1030 & ToS Bypass

17 U.S.C. § 106 Exclusive Rights

Derivative Work Violation

Direct Output Reconstruction

Attribution & CMI Removal

17 U.S.C. § 1202 DMCA

Forensic Node Inspection

Click any node in the lineage graph on the left to review token survival rates, contamination pathways, and plaintiff legal exposure.

Live Cross-Entity Accusation & Evidence Ledger

Tracking web crawler vs AI studio infringement actions

Plaintiff / Claimant	Accused AI Entity	Primary Claim	Lineage Vector	Exposure Status

Mapping AI Training Lineage and Copyright Exposure

Read the explanation

The tool simulates how ingested web tokens travel through scrubbing filters into trained model weights, linking legal claims to retained training patterns. Increasing filter scrub sensitivity aggressively purges copyrighted sequences, reducing weight attribution while boosting the defensible fair use ratio. Selecting statutory claims recalculates overall risk exposure and dynamically filters corresponding court actions in the live evidence ledger.

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