Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
This addresses the problem of ethical and trustworthy AI development for policymakers, developers, and data creators, but it is incremental as it builds on existing analysis and solutions.
The paper identifies that current practices in foundation model training data collection lack transparency, leading to issues with authenticity, consent, and provenance, and proposes adopting universal data provenance standards to address these challenges.
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.