Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?

TL;DR

This paper highlights the critical issues in data authenticity, consent, and provenance in AI training data, emphasizing the need for standardized infrastructure and transparency to develop ethical and trustworthy foundation models.

Contribution

It provides a large-scale analysis of the current data landscape, identifies infrastructure gaps, and proposes universal data provenance standards to improve responsible AI development.

Findings

Current tools are inadequate for tracing data authenticity and consent.

Regulation emphasizes transparency but lacks standardized solutions.

Universal data provenance standards can facilitate responsible AI practices.

Abstract

New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have led to challenges in tracing authenticity, verifying consent, preserving privacy, addressing representation and bias, respecting copyright, and overall developing ethical and trustworthy foundation models. In response, regulation is emphasizing the need for training data transparency to understand foundation models' limitations. Based on a large-scale analysis of the foundation model training data landscape and existing solutions, we identify the missing infrastructure to facilitate responsible foundation model development practices. We examine the current shortcomings of common tools for tracing data authenticity, consent, and documentation, and outline how policymakers, developers, and data creators can facilitate responsible foundation model development by adopting universal data provenance standards.