📌 Key Topics Include:
- Lifecycle Uses & LLM-Driven Generation
- Safety & Robustness
- Privacy, Security & Data Governance
- Fairness, Bias & Representation
- Explainability, Interpretability & Uncertainty
- Standards, Metrics & Tooling for Trustworthy Use
- Critical Perspectives on Synthetic Data

Foundation models increasingly leverage synthetic data for training while simultaneously generating synthetic datasets for downstream applications.

This workshop centers on the responsible development and use of synthetic data with and for foundation models

🗓️ Submission Deadline: October 20th, 2025 AoE

(Caught this via Terms Watch - a tool I built that monitors ToS changes across major platforms. Daily digest at termswatch.io)

This is a fascinating shift in platform power dynamics. Instead of unilateral AI scraping, we're seeing the a forced data reciprocity

Some open source devs may want LLMs to learn their code - it could help users get support.

But this reciprocity clause might have a chilling effect

Practical example: Google trains Gemini on GitHub code (everyone does - it's the world's largest code repo).

Under the new terms, GitHub could now access Google's public data - like YouTube videos - for their own AI training