7 articles tagged with #data-provenance. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
A16z-backed Story Protocol has rebranded and raised $140 million to address copyright and data rights issues in AI. The company is building an audit layer for data consent, licensing, and provenance, positioning itself at the intersection of AI and blockchain to solve intellectual property challenges.
The DATA Foundation has pivoted toward building AI training data infrastructure by launching Trace, an onchain registry designed to enhance transparency and legal compliance in AI data sourcing. This move addresses growing concerns about data provenance and copyright in AI model development, potentially establishing new standards for responsible AI training practices.
A research paper proposes blockchain as foundational infrastructure for embodied AI systems, addressing the dual challenge of securing data economies while defending against quantum computing threats. The work integrates post-quantum cryptography, cross-organizational governance, and scalable architectures to create trustworthy decentralized environments for AI-driven cyber-physical systems.
Researchers introduce MC-PDD, a black-box method to detect whether specific datasets were used to pretrain large language models by analyzing prediction patterns on masked text. The technique works through standard API access without requiring model probability distributions, enabling practical auditing of closed-source LLMs and addressing transparency concerns around proprietary training data.
Researchers introduce LLMSurgeon, a framework that reverse-engineers the pretraining data composition of Large Language Models by analyzing their generated text, addressing the opacity surrounding how foundation models are trained. The method estimates domain-level distributions across a predefined taxonomy without requiring access to actual training datasets, offering a practical auditing tool for understanding model behavior and capabilities.
Researchers present a modular, provenance-aware pipeline that converts handwritten archival tables into Knowledge Graphs while maintaining transparency through intermediate inspection points. The approach combines table structure recognition, handwriting recognition, and semantic interpretation while tracking data lineage to ensure all extracted information remains traceable to its source, addressing the opacity problem in end-to-end AI systems.
Gypscie is a new cross-platform AI artifact management system that unifies the complexity of managing machine learning models across diverse infrastructure through a knowledge graph and rule-based query language. The system streamlines the entire AI model lifecycle—from data preparation through deployment and monitoring—while enabling explainability through provenance tracking.
