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Hugging Face AI & Society Team
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Research and Policy Questions raised by (open) AI technology
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hfmlsoc's activity
yjernite
updated
a
Space
3 days ago
evijit
authored
2
papers
8 days ago
Post
1523
🤗 Just published: "Consent by Design" - exploring how we're building better consent mechanisms across the HF ecosystem!
Our research shows open AI development enables:
- Community-driven ethical standards
- Transparent accountability
- Context-specific implementations
- Privacy as core infrastructure
Check out our Space Privacy Analyzer tool that automatically generates privacy summaries of applications!
Effective consent isn't about perfect policies; it's about architectures that empower users while enabling innovation. 🚀
Read more: https://huggingface.co/blog/giadap/consent-by-design
Our research shows open AI development enables:
- Community-driven ethical standards
- Transparent accountability
- Context-specific implementations
- Privacy as core infrastructure
Check out our Space Privacy Analyzer tool that automatically generates privacy summaries of applications!
Effective consent isn't about perfect policies; it's about architectures that empower users while enabling innovation. 🚀
Read more: https://huggingface.co/blog/giadap/consent-by-design
Post
3112
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
giadap
authored
a
paper
24 days ago
Post
2338
We've all become experts at clicking "I agree" without a second thought. In my latest blog post, I explore why these traditional consent models are increasingly problematic in the age of generative AI.
I found three fundamental challenges:
- Scope problem: how can you know what you're agreeing to when AI could use your data in different ways?
- Temporality problem: once an AI system learns from your data, good luck trying to make it "unlearn" it.
- Autonomy trap: the data you share today could create systems that pigeonhole you tomorrow.
Individual users shouldn't bear all the responsibility, while big tech holds all the cards. We need better approaches to level the playing field, from collective advocacy and stronger technological safeguards to establishing "data fiduciaries" with a legal duty to protect our digital interests.
Available here: https://huggingface.co/blog/giadap/beyond-consent
I found three fundamental challenges:
- Scope problem: how can you know what you're agreeing to when AI could use your data in different ways?
- Temporality problem: once an AI system learns from your data, good luck trying to make it "unlearn" it.
- Autonomy trap: the data you share today could create systems that pigeonhole you tomorrow.
Individual users shouldn't bear all the responsibility, while big tech holds all the cards. We need better approaches to level the playing field, from collective advocacy and stronger technological safeguards to establishing "data fiduciaries" with a legal duty to protect our digital interests.
Available here: https://huggingface.co/blog/giadap/beyond-consent
sasha
authored
a
paper
about 1 month ago
evijit
authored
a
paper
about 1 month ago
yjernite
authored
a
paper
2 months ago
irenesolaiman
authored
2
papers
2 months ago
giadap
authored
a
paper
3 months ago
Post
495
From ancient medical ethics to modern AI challenges, the journey of consent represents one of humanity's most fascinating ethical evolutions. In my latest blog post, I explore how we've moved from medical paternalism to a new frontier where AI capabilities force us to rethink consent.
The "consent gap" in AI is real: while we can approve initial data use, AI systems can generate countless unforeseen applications of our personal information. It's like signing a blank check without knowing all possible amounts that could be filled in.
Should we reimagine consent for the AI age? Perhaps we need dynamic consent systems that evolve alongside AI capabilities, similar to how healthcare transformed from physician-centered authority to patient autonomy.
Curious to hear your thoughts: how can we balance technological innovation with meaningful user sovereignty over digital identity?
Read more: https://huggingface.co/blog/giadap/evolution-of-consent
The "consent gap" in AI is real: while we can approve initial data use, AI systems can generate countless unforeseen applications of our personal information. It's like signing a blank check without knowing all possible amounts that could be filled in.
Should we reimagine consent for the AI age? Perhaps we need dynamic consent systems that evolve alongside AI capabilities, similar to how healthcare transformed from physician-centered authority to patient autonomy.
Curious to hear your thoughts: how can we balance technological innovation with meaningful user sovereignty over digital identity?
Read more: https://huggingface.co/blog/giadap/evolution-of-consent
Post
2414
🤗👤 💻 Speaking of AI agents ...
...Is easier with the right words ;)
My colleagues @meg @evijit @sasha and @giadap just published a wonderful blog post outlining some of the main relevant notions with their signature blend of value-informed and risk-benefits contrasting approach. Go have a read!
https://huggingface.co/blog/ethics-soc-7
...Is easier with the right words ;)
My colleagues @meg @evijit @sasha and @giadap just published a wonderful blog post outlining some of the main relevant notions with their signature blend of value-informed and risk-benefits contrasting approach. Go have a read!
https://huggingface.co/blog/ethics-soc-7
Post
2245
🇪🇺 Policy Thoughts in the EU AI Act Implementation 🇪🇺
There is a lot to like in the first draft of the EU GPAI Code of Practice, especially as regards transparency requirements. The Systemic Risks part, on the other hand, is concerning for both smaller developers and for external stakeholders.
I wrote more on this topic ahead of the next draft. TLDR: more attention to immediate large-scale risks and to collaborative solutions supported by evidence can help everyone - as long as developers disclose sufficient information about their design choices and deployment contexts.
Full blog here, based on our submitted response with @frimelle and @brunatrevelin :
https://huggingface.co/blog/yjernite/eu-draft-cop-risks#on-the-proposed-taxonomy-of-systemic-risks
There is a lot to like in the first draft of the EU GPAI Code of Practice, especially as regards transparency requirements. The Systemic Risks part, on the other hand, is concerning for both smaller developers and for external stakeholders.
I wrote more on this topic ahead of the next draft. TLDR: more attention to immediate large-scale risks and to collaborative solutions supported by evidence can help everyone - as long as developers disclose sufficient information about their design choices and deployment contexts.
Full blog here, based on our submitted response with @frimelle and @brunatrevelin :
https://huggingface.co/blog/yjernite/eu-draft-cop-risks#on-the-proposed-taxonomy-of-systemic-risks
- 2 replies
giadap
authored
a
paper
11 months ago
evijit
authored
a
paper
about 1 year ago
yjernite
authored
a
paper
about 1 year ago