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Classement compar:IA : des votes des utilisateurs au classement participatif des modèles
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Bruna Trevelin
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Classement compar:IA : des votes des utilisateurs au classement participatif des modèles
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Toward Community-Governed Safety
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What Open-Source Developers Need to Know about the EU AI Act's Rules for GPAI Models
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What Open-Source Developers Need to Know about the EU AI Act's Rules for GPAI Models
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I've been posting bits and pieces about this research, but now I can finally say: new paper alert 🚨
My colleague @brunatrevelin and I just shared a paper exploring why traditional consent frameworks are breaking down in AI contexts (forthcoming chapter in a collective book).
The current model places impossible burdens on users to manage countless consent decisions. Meanwhile, AI systems learn to mimic our voices and writing styles from data we unknowingly provided years ago.
What's next? We need to shift from individual responsibility to collective accountability.
This means:
- Organizations designing systems that respect human agency by default
- Developers building ethics into models from the start
- Policymakers creating frameworks beyond minimal compliance
Blog post: https://huggingface.co/blog/giadap/consentful-ai
Paper: Can AI be Consentful? (2507.01051)
My colleague @brunatrevelin and I just shared a paper exploring why traditional consent frameworks are breaking down in AI contexts (forthcoming chapter in a collective book).
The current model places impossible burdens on users to manage countless consent decisions. Meanwhile, AI systems learn to mimic our voices and writing styles from data we unknowingly provided years ago.
What's next? We need to shift from individual responsibility to collective accountability.
This means:
- Organizations designing systems that respect human agency by default
- Developers building ethics into models from the start
- Policymakers creating frameworks beyond minimal compliance
Blog post: https://huggingface.co/blog/giadap/consentful-ai
Paper: Can AI be Consentful? (2507.01051)
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Can AI Be Consentful? Rethinking Permission in the Age of Synthetic Everything
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Open Source AI: A Cornerstone of Digital Sovereignty
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The HF Policy Team submitted our response to the 2025 National Artificial Intelligence (AI) Research and Development (R&D) Strategic Plan.
Blog (with link to full pdf response):
https://huggingface.co/blog/evijit/us-ai-research-strategy-rfi
Blog (with link to full pdf response):
https://huggingface.co/blog/evijit/us-ai-research-strategy-rfi
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AI Policy @🤗: Response to the 2025 National AI R&D Strategic Plan
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Reduce, Reuse, Recycle: Why Open Source is a Win for Sustainability
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Consent by Design: Approaches to User Data in Open AI Ecosystems
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Hugging Face to sell open-source robots thanks to Pollen Robotics acquisition 🤖
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Today in Privacy & AI Tooling - introducing a nifty new tool to examine where data goes in open-source apps on 🤗
HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data 📚🔍
That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK 🙌
The app works in three stages:
1. Download all code files
2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1)
3. Summarize the app's main functionality and data journeys (screen 2)
4. Build a Privacy TLDR with those inputs
It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints 🤗
Note that this is a POC, lots of exciting work to do to make it more robust, so:
- try it: yjernite/space-privacy
- reach out to collab: yjernite/space-privacy
HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data 📚🔍
That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK 🙌
The app works in three stages:
1. Download all code files
2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1)
3. Summarize the app's main functionality and data journeys (screen 2)
4. Build a Privacy TLDR with those inputs
It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints 🤗
Note that this is a POC, lots of exciting work to do to make it more robust, so:
- try it: yjernite/space-privacy
- reach out to collab: yjernite/space-privacy