Rethinking AI data: From scraping to sustainable and ethical data sharing
OECD-level thinking on AI training data governance shapes the international norms Australian agencies and regulators will eventually need to engage with.
Key points
- OECD's VIADUCT project examines ethical AI training data sharing as an alternative to web scraping.
- Addresses copyright, GDPR, and trust frameworks as constraints shaping sustainable AI data ecosystems.
- Extracted text is a brief teaser only - substantive content requires engagement at source.
Implications for Australian agencies
- Monitor Policy teams working on AI data governance or Australia's AI regulatory settings may want to monitor VIADUCT outputs for emerging OECD norms on ethical data sourcing.
- Consider Agencies procuring AI systems could consider how training data provenance and ethical sourcing requirements might feature in future procurement criteria or risk assessments.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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Weekly digest, 30 March 2026
"Rethinking AI data: From scraping to sustainable and ethical data sharing"
Source: OECD AI Wonk Blog
Published: 31 March 2026
URL: https://wp.oecd.ai/rethinking-ai-data-from-scraping-to-sustainable-and-ethical-data-sharing/
An OECD AI Wonk Blog post introduces the VIADUCT project, which explores alternatives to web scraping for AI training data, framing data scarcity as a governance and ethics challenge rather than a purely technical one. It touches on copyright constraints, GDPR implications, and the need for trust and fairness in data-sharing arrangements to support sustainable AI ecosystems. The extracted content is a short teaser; the full analysis is available at source and warrants direct engagement for those working on AI data governance or procurement policy.
Implications for Australian agencies:
- [Monitor] Policy teams working on AI data governance or Australia's AI regulatory settings may want to monitor VIADUCT outputs for emerging OECD norms on ethical data sourcing.
- [Consider] Agencies procuring AI systems could consider how training data provenance and ethical sourcing requirements might feature in future procurement criteria or risk assessments.
Retrieved from SIMS, 18 July 2026.