Bibliographic record
Abstract
This is the report from the project The Role of Data in AI, which was commissioned by the Data Governance Working Group of the Global Partnership of AI (GPAI). The consortium was led by the Digital Curation Centre, with partners Trilateral Research and The School of Informatics, the University of Edinburgh. The report digs more deeply into the issues raised within the Data Governance Framework (see below), and identifies areas where GPAI could make an impact in deepening international collaboration. It covers the following areas: Al development and the role of data at each step; Data types used in AI development; Data characteristics that influence the process or outcome of Al development; Socio-ethical, economic and environmental impacts of data in Al; Law and transparency as modifiers to impacts of data in Al; Availability of accessibility to data for Al development; data quality and challenges in three fields (pandemic response, human language technologies for under-resourced languages, and AI applications in the criminal justice system); and recommendations on where GPAI could enhance international collaboration on data governance. The report was prepared for GPAIs first summit in Montréal in December 2020 and presented along with the following related documents from the Data Governance WG: A Framework Paper for GPAI's Work on Data Governance, November 2020. Data Governance Working Group Report, November 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".