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Big Science Social Impact Evaluation for Bias and Stereotypes

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datasets, social impact, bias, evaluation

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jjzha  authored a paper about 7 hours ago
HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings
frimelle  authored a paper 8 days ago
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions
frimelle  authored a paper 8 days ago
Presumed Cultural Identity: How Names Shape LLM Responses
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LanguageShades's activity

fdaudens 
posted an update about 5 hours ago
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What if AI becomes as ubiquitous as the internet, but runs locally and transparently on our devices?

Fascinating TED talk by @thomwolf on open source AI and its future impact.

Imagine this for AI: instead of black box models running in distant data centers, we get transparent AI that runs locally on our phones and laptops, often without needing internet access. If the original team moves on? No problem - resilience is one of the beauties of open source. Anyone (companies, collectives, or individuals) can adapt and fix these models.

This is a compelling vision of AI's future that solves many of today's concerns around AI transparency and centralized control.

Watch the full talk here: https://www.ted.com/talks/thomas_wolf_what_if_ai_just_works
jjzha 
authored a paper about 7 hours ago
fdaudens 
posted an update 2 days ago
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Is this the best tool to extract clean info from PDFs, handwriting and complex documents yet?

Open source olmOCR just dropped and the results are impressive.

Tested the free demo with various documents, including a handwritten Claes Oldenburg letter. The speed is impressive: 3000 tokens/second on your own GPU - that's 1/32 the cost of GPT-4o ($190/million pages). Game-changer for content extraction and digital archives.

To achieve this, Ai2 trained a 7B vision language model on 260K pages from 100K PDFs using "document anchoring" - combining PDF metadata with page images.

Best part: it actually understands document structure (columns, tables, equations) instead of just jumbling everything together like most OCR tools. Their human eval results back this up.

👉 Try the demo: https://olmocr.allenai.org

Going right into the AI toolkit: JournalistsonHF/ai-toolkit
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fdaudens 
posted an update 4 days ago
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🚀 Just launched: A toolkit of 20 powerful AI tools that journalists can use right now - transcribe, analyze, create. 100% free & open-source.

Been testing all these tools myself and created a searchable collection of the most practical ones - from audio transcription to image generation to document analysis. No coding needed, no expensive subscriptions.

Some highlights I've tested personally:
- Private, on-device transcription with speaker ID in 100+ languages using Whisper
- Website scraping that just works - paste a URL, get structured data
- Local image editing with tools like Finegrain (impressive results)
- Document chat using Qwen 2.5 72B (handles technical papers well)

Sharing this early because the best tools come from the community. Drop your favorite tools in the comments or join the discussion on what to add next!

👉 JournalistsonHF/ai-toolkit
fdaudens 
posted an update 7 days ago
frimelle 
authored 2 papers 8 days ago
frimelle 
posted an update 8 days ago
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2364
What’s in a name? More than you might think, especially for AI.
Whenever I introduce myself, people often start speaking French to me, even though my French is très basic. It turns out that AI systems do something similar:
Large language models infer cultural identity from names, shaping their responses based on presumed backgrounds. But is this helpful personalization or a reinforcement of stereotypes?
In our latest paper, we explored this question by testing DeepSeek, Llama, Aya, Mistral-Nemo, and GPT-4o-mini on how they associate names with cultural identities. We analysed 900 names from 30 cultures and found strong assumptions baked into AI responses: some cultures were overrepresented, while others barely registered.
For example, a name like "Jun" often triggered Japan-related responses, while "Carlos" was linked primarily to Mexico, even though these names exist in multiple countries. Meanwhile, names from places like Ireland led to more generic answers, suggesting weaker associations in the training data.
This has real implications for AI fairness: How should AI systems personalize without stereotyping? Should they adapt at all based on a name?
Work with some of my favourite researchers: @sidicity Arnav Arora and @IAugenstein
Read the full paper here: Presumed Cultural Identity: How Names Shape LLM Responses (2502.11995)
jjzha 
authored 3 papers 9 days ago
fdaudens 
posted an update 10 days ago
fdaudens 
posted an update 12 days ago
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2268
Will we soon all have our own personalized AI news agents? And what does it mean for journalism?

Just built a simple prototype based on the Hugging Face course. It lets you get customized news updates on any topic.

Not perfect yet, but you can see where things could go: we'll all be able to build personalized AI agents that curate & analyze news for each of us. And users who could decide to build custom news products for their needs, such as truly personalized newsletters or podcasts.

The implications for both readers & news organizations are significant. To name a few:
- Will news articles remain the best format for informing people?
- What monetization model will work for news organizations?
- How do you create an effective conversion funnel?

👉 Try it here: fdaudens/my-news-agent (Code is open-source)
👉 Check out the course: https://huggingface.co./learn/agents-course/unit0/introduction
fdaudens 
posted an update 14 days ago
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2117
🔊 Meet Kokoro Web - Free, ML speech synthesis on your computer, that'll make you ditch paid services!

28 natural voices, unlimited generations, and WebGPU acceleration. Perfect for journalists and content creators.

Test it with full articles—sounds amazingly human! 🎯🎙️

Xenova/kokoro-web
fdaudens 
posted an update 15 days ago
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⭐️ The AI Energy Score project just launched - this is a game-changer for making informed decisions about AI deployment.

You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI.

Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything.

166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test.

Why this matters:
- Teams can pick efficient models that still get the job done
- Developers can optimize for energy use from day one
- Organizations can finally predict their AI environmental impact

If you're building with AI at any scale, definitely worth checking out.

👉 leaderboard: https://lnkd.in/esrSxetj
👉 blog post: https://lnkd.in/eFJvzHi8

Huge work led by @sasha with @bgamazay @yjernite @sarahooker @regisss @meg
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giadap 
authored a paper 18 days ago
fdaudens 
posted an update 18 days ago
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🔥 Video AI is taking over! Out of 17 papers dropped on Hugging Face today, 6 are video-focused - from Sliding Tile Attention to On-device Sora. The race for next-gen video tech is heating up! 🎬🚀
fdaudens 
posted an update 22 days ago
frimelle 
posted an update 24 days ago
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I was quoted in an article about the French Lucie AI in La Presse. While I love the name for obvious reasons 👀 there were still a lot of problems with the model and how and when it was deployed. Nevertheless seeing new smaller models being developed is an exciting direction for the next years of AI development to come!

https://www.lapresse.ca/affaires/techno/2025-02-02/radioscopie/lucie-l-ia-francaise-qui-ne-passe-pas-le-test.php

Also fun to see my comments in French.
frimelle 
posted an update 24 days ago
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Seeing AI develop has been a wild ride, from trying to explain why we'd bother to generate a single sentence with a *neural network* to explaining that AI is not a magic, all-knowing box. The recent weeks and months have been a lot of talking about how AI works; to policy makers, to other developers, but also and mainly friends and family without a technical background.

Yesterday, the first provisions of the EU AI Act came into force, and one of the the key highlights are the AI literacy requirements for organisations deploying AI systems. This isn't just a box-ticking exercise. Ensuring that employees and stakeholders understand AI systems is crucial for fostering responsible and transparent AI development. From recognising biases to understanding model limitations, AI literacy empowers individuals to engage critically with these technologies and make informed decisions.

In the context of Hugging Face, AI literacy has many facets: allowing more people to contribute to AI development, providing courses and documentation to ensuring access is possible, and accessible AI tools that empower users to better understand how AI systems function. This isn't just a regulatory milestone; it’s an opportunity to foster a culture where AI literacy becomes foundational, enabling stakeholders to recognise biases, assess model limitations, and engage critically with technology.

Embedding these principles into daily practice, and eventually extending our learnings in AI literacy to the general public, is essential for building trustworthy AI that aligns with societal values.
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