Bridging the Data Provenance Gap Across Text, Speech and Video

Ahmad Mustafa Anis; Alex Pentland; An Dinh; Ariel N. Lee; Caiming Xiong; Campbell S. Lund; Christopher Klamm; Damien Sileo; Da Yin; Deividas Mataciunas

REVIEW 3 major objections 6 minor 3 cited by

Bridging the Data Provenance Gap Across Text, Speech and Video

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A multimodal audit of nearly 4,000 datasets argues that dataset licenses drastically understate the legal constraints on AI training data, because over 80% of the content in widely used text, speech, and video datasets carries…

desk verdict First modality-spanning provenance audit; useful reference, but 'over 80%' overstates speech (78.6%) and the manual source coding needs validation before the headline numbers are taken as fact. read the letter →

arxiv 2412.17847 v2 pith:IFMWDFZY submitted 2024-12-19 cs.AI cs.CLcs.CYcs.LGcs.MM

Shayne Longpre , Nikhil Singh , Manuel Cherep , Kushagra Tiwary , Joanna Materzynska , William Brannon , Robert Mahari , Naana Obeng-Marnu

show 35 more authors

Manan Dey Mohammed Hamdy Nayan Saxena Ahmad Mustafa Anis Emad A. Alghamdi Vu Minh Chien Da Yin Kun Qian Yizhi Li Minnie Liang An Dinh Shrestha Mohanty Deividas Mataciunas Tobin South Jianguo Zhang Ariel N. Lee Campbell S. Lund Christopher Klamm Damien Sileo Diganta Misra Enrico Shippole Kevin Klyman Lester JV Miranda Niklas Muennighoff Seonghyeon Ye Seungone Kim Vipul Gupta Vivek Sharma Xuhui Zhou Caiming Xiong Luis Villa Stella Biderman Alex Pentland Sara Hooker Jad Kabbara

This is my paper · ORCID

classification cs.AIcs.CLcs.CYcs.LGcs.MM

keywords dataprovenancedatasetlicensingtrainingauditmultimodaldatasetssourcerestrictionsnon-commercialgeographicalrepresentationlinguistic

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the public record of how AI training data may be used is systematically misleading. Auditing nearly 4,000 text, speech, and video datasets released between 1990 and 2024, the authors find that while fewer than a third of datasets carry restrictive licenses, the underlying source content is far more constrained: 99.8% of text tokens, 78% of speech hours, and 99% of video hours carry non-commercial restrictions that come from the sources — websites, social media platforms, or generative models — the data was drawn from. Because those source-level restrictions are usually dropped when datasets are re-packaged, a permissive dataset license does not tell practitioners what they may actually use. A sympathetic reader would care because the finding puts a measurable legal shadow over the common practice of assembling large training corpora from web crawls, YouTube, and synthetic model outputs, and the released audit lets individual developers trace a dataset's chain of provenance for themselves.

What carries the argument

The load-bearing instrument is a manual provenance-tracing protocol that codes each dataset on two axes at once. The dataset license is categorized as Commercial, Non-commercial/Academic, or Unspecified, while each underlying source — every website, platform, or model the content came from — is coded as Unrestricted, Unspecified, Source Closed, or Model Closed, following a defined taxonomy that covers terms of service, acceptable-use policies, and anti-crawling clauses. A dataset's overall terms status is set to the strictest of its sources, and the crosstab of license against terms is then reported both by dataset count and weighted by tokens or hours, which is what produces the headline mismatch figures.

What would settle it

Take a random sample of a few hundred datasets from the released audit, have a second independent team re-annotate source restrictions from the same lineage, and compute agreement; low agreement on the Source Closed and Model Closed categories would undercut the 80%-plus mismatch claim. A separate check: recompute the 99.8% text, 78% speech, and 99% video figures with the single largest collection per modality removed (for example the roughly 370k-hour YouTube-sourced speech corpus), since one re-coded giant could shift the ecosystem totals.

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Extended reading notes

Core claim

The paper's central claim is a mismatch between two layers of restriction: dataset licenses and source terms. Counting datasets, only 25% of text, 33% of speech, and 32% of video datasets are licensed non-commercially; weighted by content, those figures are 21%, 26%, and 33%. But when the authors trace each dataset back to its original sources and classify the sources' licenses or terms of service, 99.8% of text tokens, 78% of speech hours, and 99% of video hours carry some non-commercial restriction at the source level, and the license-versus-terms mismatch covers 79% of text tokens, 55% of speech hours, and 65% of video hours. The paper further claims that since 2019 multimodal training has overwhelmingly moved to web-crawled, synthetic, and social-media sources (YouTube alone supplies roughly 71% of video data and 69% of speech data), and that although the absolute number of languages and countries represented keeps rising, the relative dispersion measured by Gini coefficients has not significantly improved since 2013 — Western concentration persists despite diversification at the margins.

Load-bearing premise

The central percentages depend on the manual classification of each dataset's sources as Unrestricted, Unspecified, Source Closed, or Model Closed, and the paper reports no inter-annotator agreement or independent validation of that coding, so a systematic bias in it would move every headline number.

Editorial extensions

If this is right

Developers who filter training data by dataset license alone will systematically misjudge the restrictions on the content, since source-level terms bind more than 80% of content in each modality.
Because the strictest-source rule intensifies at the collection level, large re-packaged text collections hide commercially usable subsets that practitioners cannot easily extract.
The concentration of speech and video on a single video platform means platform terms, not dataset licenses, are the effective gatekeepers of most audio-visual training data.
If the source coding is correct, the permissive commons for multimodal training reduces to the small residual of content that is both commercially licensed and sourced from unrestricted sources — under 1% of text and video content and about 5% of speech hours.
Relative geographical and linguistic representation has been flat for a decade, so adding more languages and countries at the margins does not by itself reduce concentration.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

One implication the paper leaves implicit: if source terms bind, then a 'clean' multimodal training run under current terms would have to be assembled almost entirely from a short list of explicitly permissive sources, and the paper's released tables make that set enumerable.
A testable extension: apply the same two-axis coding to datasets released after April 2024 to see whether the license-versus-source mismatch grows as synthetic outputs and short-video platforms enter the training mix.
The mismatch framing points to a fork the paper deliberately does not resolve: either source terms are largely unenforceable against training (shifting risk to copyright law itself), or a large fraction of existing multimodal training is already in breach of terms — future litigation will pick the branch.
Because the volume-weighted percentages are driven by a handful of giant collections, a sensitivity analysis that recomputes the headline figures with the largest collection per modality removed would show how robust the ecosystem-level claims actually are.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First, the useful part: this is the first audit I know that traces provenance, license terms, and geographic/linguistic representation across text, speech, and video from the same methodology. Roughly 4,000 datasets, 1990–2024, with per-modality source and restriction tables and longitudinal Gini coefficients. That is a real reference contribution, and the appendix tables are extensive. If you work on data governance or dataset documentation, you will want this on hand.

The paper is honest about its assumptions—scope choice, creator-country mapping, the fact that 'relative representation hasn't improved' is measured by Gini, not absolute counts. Those parts hold up.

The soft spots, in order. First, the abstract says 'over 80% of the source content' is restricted, but their own Table 3 gives speech 78.6%. The body says 78%. That is a mismatch, a public-facing overstatement, and it should be fixed.

Second, the central restriction numbers rest on manual coding of each source as Unrestricted, Unspecified, Source Closed, or Model Closed. No inter-annotator agreement, no gold-standard check, no robustness test is reported. For a headline like 99.8% of text tokens restricted, systematic annotator bias would move the number materially. This is the main reason I would not call the quantitative claim airtight. It is not fatal—the taxonomy is inherited from their prior published audit, and the direction of bias is not obvious—but a reliability study or a sensitivity analysis is needed before I'd treat 99.8% as fact rather than estimate.

Third, Figure 4 shows 95% CIs for Gini but does not say how they were computed. And the audit data and code is promised 'on release' but no link or commit hash is provided. These are minor at this stage, but for a descriptive audit whose value is the data, the release should be part of the submission.

The citation pattern is fine; self-citations are to their own earlier audits and the taxonomy, which is legitimate.

Bottom line: the central argument—that the public data commons is much more legally constrained than dataset licenses suggest—survives reading, with the speech caveat. The paper deserves a serious referee. I'd send it out; I'd ask for the reliability check and a corrected abstract.

Who is this for? Regulators, dataset builders, responsible-AI researchers, and anyone doing licensing analysis. I would bring it to a reading group and would cite it in my own work.

Referee Report

3 major / 6 minor

Summary. This paper presents a large-scale manual audit of 3,916 public text, speech, and video datasets released between 1990 and 2024, tracing sourcing trends, license and source-term restrictions, and geographic and linguistic representation. The authors report three headline findings: (1) multimodal training data increasingly comes from web-crawled, social media, and synthetic sources; (2) although fewer than one-third of datasets carry restrictive licenses, over 80% of the underlying source content carries non-commercial restrictions; and (3) absolute language and country counts have risen since 2013 but relative measures of geographic and multilingual inequality have not significantly improved. The paper includes extensive appendix tables, a detailed taxonomy, and a promised public release of the audit data and code. The central quantitative claims are descriptive measurements rather than fitted-model outputs, but the main restriction percentages depend entirely on manually assigned source-level labels whose reliability is not reported.

Significance. If the headline measurement is correct, the paper provides an important and policy-relevant result: the effective legal constraints on widely used training data are much stronger than dataset licenses alone suggest. The work is the first multimodal provenance audit at this scale, and the appendix tables and attribution cards are a substantial community resource. The paper is appropriately cautious in describing licenses and terms as signals rather than enforceable legal determinations, and it avoids overfitting by presenting descriptive statistics rather than model-based claims. The empirical contribution would be strengthened by the promised release of annotations and code, which is an explicit strength if delivered. However, the central 'over 80%' claim is not yet fully supported: it is internally inconsistent with the paper's own Table 3 for speech, and the underlying manual classifications have no reported reliability validation.

major comments (3)

[Section 2, 'Annotation Features & Methodology'; Tables 3 and 4] The central quantitative claims—99.8% of text tokens, 78.6% of speech hours, and 99.1% of video hours carrying source restrictions—are direct tabulations of the manually assigned labels in Table 2, yet the paper reports no inter-annotator agreement, no gold-standard validation, and no sensitivity analysis for ambiguous cases. A systematic bias in coding (for example, treating 'no information found' as Restricted rather than Unspecified) would directly change the headline percentages and the abstract's 'over 80%' conclusion. The manuscript states that 'All annotations and analysis code will be made publicly available on release,' but no link or commit hash is given, so independent verification is not currently possible. Please add a reliability study, such as dual annotation with agreement statistics on a representative subsample or an audit of ambiguous cases, and provide the data and code artifact at submission time.
[Abstract and Section 3.2 (Table 3)] The abstract claims that 'over 80% of the source content in widely-used text, speech, and video datasets carry non-commercial restrictions,' and the introduction states 'over 80% of content from each modality.' Table 3 reports 78.6% for speech hours, and Section 3.2 itself says '78%' for speech. The claim is therefore internally inconsistent with the paper's own appendix data. The wording should be corrected to 'roughly 80%,' 'over 78%,' or the computation should be adjusted so that the abstract matches the reported tables.
[Section 3.3, Figure 4] The claim that geographical and linguistic representation 'has not significantly improved' rests on Gini coefficients with 95% confidence intervals and statements about significance at the p = 0.05 level, but the method for computing these intervals and tests is not described anywhere in the paper or appendix, and no code is provided. Without knowing whether the intervals come from a bootstrap, jackknife, or analytic approach, the 'not significant' conclusion is not verifiable. Please specify the estimator and test procedure, or downgrade the claim to a descriptive statement about observed trends.

minor comments (6)

[Section 2.1 vs Table 1] Section 2.1 reports 3,713 text datasets from 108 collections, while Table 1 reports 3,717 text datasets; these counts should be reconciled.
[References] References [11] and [12] are the same paper by Buolamwini and Gebru, and the Common Voice citation appears twice, once as [13] and again as [20]; these duplicates should be merged.
[Table 3 caption] The caption says the table is a breakdown 'across datasets' but the cells are percentages of total tokens or hours; clarify that the units are shares of content, not dataset counts, to avoid confusion with Table 4.
[Figure 2 caption and Section 3.2] The phrase 'bare restrictions' appears in the Figure 2 caption and in the body text; this should be 'bear restrictions'.
[Introduction, finding 2] The phrase 'undocumented restrictions in the dataset's sources' is imprecise: the source restrictions are often documented in terms of service, but not in the dataset license; consider wording such as 'source-level restrictions not reflected in dataset licenses.'
[Section 3.1] The comparison of average synthetic and natural dataset lengths (1,756 vs 1,065 tokens) would benefit from sample sizes and a measure of dispersion to support the word 'notably.'

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the reported percentages are new manual measurements, and the inherited taxonomy from the authors' prior audit is not load-bearing.

full rationale

The paper's central claims are measurements from manual annotations, not derivations from fitted parameters or self-referential definitions. The headline restriction percentages (e.g., 99.8% text, 78.6% speech, 99.1% video in Table 3) are computed by classifying each dataset source into Unrestricted, Unspecified, Source Closed, or Model Closed per Table 2, then aggregating by token or hour counts. Nothing in Table 2 is defined in terms of the headline result, and no parameter is fitted to a subset of the data and then 'predicted' on a closely related quantity; the annotation labels are the input and the source-restriction percentages are the output, which is the normal structure of a descriptive audit rather than a circular derivation. The adoption of the prior taxonomy and annotation process from the authors' own work, Longpre et al. [123], is a methodological inheritance and is explicitly acknowledged; it does not function as an unverified uniqueness theorem that forces the conclusion, and the current paper extends the taxonomy to source terms for speech and video. Other self-citations such as [122]-[125] provide context and framing, but the load-bearing evidence is the new dataset-level annotation, which is independently checkable once released. The most serious concerns are correctness and robustness issues rather than circularity: no inter-annotator agreement or gold-standard validation is reported for the manual source coding, the abstract's 'over 80%' claim sits uneasily with the 78.6% speech figure in Table 3, and the annotation code is promised rather than linked, as the paper states that 'all annotations and analysis code will be made publicly available on release.' These limitations affect reliability and verifiability, but they do not make the derivation circular.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is an empirical audit rather than a derivation, so there are no fitted parameters or invented entities. The load-bearing inputs are the curated dataset sample and the manual annotation decisions, listed as domain assumptions.

assumptions (3)

domain assumption The curated dataset list (from HuggingFace, surveys, and expert review) is representative of widely used public datasets
Section 2 "Scope & Dataset Selection" defines inclusion by popularity and expert supplementation; analyses generalize to the ecosystem.
domain assumption Manual source-term annotations follow the Table 2 taxonomy consistently without measurable disagreement
Section 2 "Annotation Features & Methodology" describes expert annotation but reports no inter-annotator agreement.
ad hoc to paper The Gini coefficient confidence intervals and p-value statements in Figure 4 are computed by an unspecified but valid method
Section 3.3 reports 95% CIs and p=0.05 significance tests but does not describe the bootstrap or test procedure.

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Cite this review

Pith. "Pith review of Bridging the Data Provenance Gap Across Text, Speech and Video." pith.science (2026). https://pith.science/paper/IFMWDFZY

@misc{pith2026241217847,
  author       = {Pith},
  title        = {Pith review of: Bridging the Data Provenance Gap Across Text, Speech and Video},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IFMWDFZY}},
  note         = {Machine review of arXiv:2412.17847}
}

read the original 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.

Figures

Figures reproduced from arXiv: 2412.17847 by the authors.

**Figure 2.** The distribution of restrictions from dataset [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** The geographical distribution of countries (world maps) and continents (table) represented [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: The cumulative totals (left) of languages and countries represented in the data over time, and [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: The distribution of creator organizations by modality. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 6.** Figure 6: The distribution of dataset sizes for each modality. Most text data collections are between [PITH_FULL_IMAGE:figures/full_fig_p021_6.png]

**Figure 7.** Figure 7: The task distribution of datasets, across modalities. Post-training text and video datasets [PITH_FULL_IMAGE:figures/full_fig_p022_7.png]

**Figure 6.** Figure 6: However, for Figure 2 we draw the distinction between collection and dataset metrics, as [PITH_FULL_IMAGE:figures/full_fig_p022_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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A new 8TB openly-licensed text corpus trains 7B LLMs that are competitive with Llama 1/2, showing that performant models need not depend on unlicensed web data.
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BRoverbs -- Measuring how much LLMs understand Portuguese proverbs
cs.CL 2025-09 conditional novelty 5.0 of 10

BRoverbs lets researchers test whether language models understand Portuguese proverbs; commercial models nearly master it, small models often guess randomly.

Reference graph

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Reviewed August 11, 2026 · model on record in the stance chip above.

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