Bridging the Data Provenance Gap Across Text, Speech and Video

TL;DR

This study provides a comprehensive longitudinal analysis of over 4000 public datasets across text, speech, and video modalities, revealing trends in data sourcing, restrictions, and geographic and linguistic representation, highlighting gaps in diversity and transparency.

Contribution

It is the first extensive longitudinal audit across multiple modalities, offering detailed insights into dataset sourcing, licensing restrictions, and representation trends in AI training data.

Findings

Web-crawled and social media data dominate training sets since 2019.

Most datasets contain non-commercial restrictions, despite fewer being restrictively licensed.

Geographical and linguistic diversity in datasets has not significantly improved since 2013.

Abstract

Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established datasets beyond text. In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities--popular text, speech, and video datasets--from their detailed sourcing trends and use restrictions to their geographical and linguistic representation. Our manual analysis covers nearly 4000 public datasets between 1990-2024, spanning 608 languages, 798 sources, 659 organizations, and 67 countries. We find that multimodal machine learning applications have overwhelmingly turned to web-crawled, synthetic, and social media platforms, such as YouTube, for their training sets, eclipsing all other sources since 2019. Secondly, tracing the chain of dataset derivations we find that while less than 33% of datasets are restrictively licensed, over 80% of the source content in widely-used text, speech, and video datasets, carry non-commercial restrictions. Finally, counter to the rising number of languages and geographies represented in public AI training datasets, our audit demonstrates measures of relative geographical and multilingual representation have failed to significantly improve their coverage since 2013. We believe the breadth of our audit enables us to empirically examine trends in data sourcing, restrictions, and Western-centricity at an ecosystem-level, and that visibility into these questions are essential to progress in responsible AI. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire multimodal audit, allowing practitioners to trace data provenance across text, speech, and video.