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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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meg  updated a dataset 3 days ago
LanguageShades/BiasShades
jjzha  authored a paper 10 days ago
How Do Hackathons Foster Creativity? Towards AI Collaborative Evaluation of Creativity at Scale
jjzha  authored a paper 20 days ago
HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings
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LanguageShades's activity

fdaudens 
posted an update about 15 hours ago
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🔊 Meet Orpheus: A breakthrough open-source TTS model that matches human-level speech with empathy & emotion.
- Available in 4 sizes (150M-3B parameters)
- delivers ultra-fast streaming
- zero-shot voice cloning.
- Apache 2.0 license

canopylabs/orpheus-tts-67d9ea3f6c05a941c06ad9d2
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fdaudens 
posted an update 2 days ago
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Want to build useful newsroom tools with AI? We’re launching a Hugging Face x Journalism Slack channel where journalists turn AI concepts into real newsroom solutions.

Inside the community:
✅ Build open-source AI tools for journalism
✅ Get direct help from the community
✅ Stay updated on new models and datasets
✅ Learn from other journalists’ experiments and builds

The goal? Go from “I read about AI” to “I built an AI tool that supercharged my newsroom.” —no more learning in isolation.

Join us! https://join.slack.com/t/journalistson-tnd8294/shared_invite/zt-30vsmhk4w-dZpeMOoxdhCvfNsqtspPUQ (Please make sure to use a clear identity—no teddybear85, for example 😉)

(If you know people who might be interested, tag them below! The more minds we bring in, the better the tools we build.)
meg 
updated a dataset 3 days ago
fdaudens 
posted an update 3 days ago
fdaudens 
posted an update 7 days ago
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🤯 Gemma 3's image analysis blew me away!

Tested 2 ways to extract airplane registration numbers from photos with 12B model:

1️⃣ Gradio app w/API link (underrated feature IMO) + ZeroGPU infra on Hugging Face in Google Colab. Fast & free.

2️⃣ LMStudio + local processing (100% private). Running this powerhouse on a MacBook w/16GB RAM is wild! 🚀

Colab: https://colab.research.google.com/drive/1YmmaP0IDEu98CLDppAAK9kbQZ7lFnLZ1?usp=sharing
fdaudens 
posted an update 8 days ago
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Ever wanted 45 min with one of AI’s most fascinating minds? Was with @thomwolf at HumanX Vegas. Sharing my notes of his Q&A with the press—completely changed how I think about AI’s future:

1️⃣ The next wave of successful AI companies won’t be defined by who has the best model but by who builds the most useful real-world solutions. "We all have engines in our cars, but that’s rarely the only reason we buy one. We expect it to work well, and that’s enough. LLMs will be the same."

2️⃣ Big players are pivoting: "Closed-source companies—OpenAI being the first—have largely shifted from LLM announcements to product announcements."

3️⃣ Open source is changing everything: "DeepSeek was open source AI’s ChatGPT moment. Basically, everyone outside the bubble realized you can get a model for free—and it’s just as good as the paid ones."

4️⃣ Product innovation is being democratized: Take Manus, for example—they built a product on top of Anthropic’s models that’s "actually better than Anthropic’s own product for now, in terms of agents." This proves that anyone can build great products with existing models.

We’re entering a "multi-LLM world," where models are becoming commoditized, and all the tools to build are readily available—just look at the flurry of daily new releases on Hugging Face.

Thom's comparison to the internet era is spot-on: "In the beginning you made a lot of money by making websites... but nowadays the huge internet companies are not the companies that built websites. Like Airbnb, Uber, Facebook, they just use the internet as a medium to make something for real life use cases."

Love to hear your thoughts on this shift!
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fdaudens 
posted an update 9 days ago
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🔥The Open R1 team just dropped OlympicCoder and it's wild:

- 7B model outperforms Claude 3.7 Sonnet on IOI benchmark (yes, 7B!!)
- 32B crushes all open-weight models tested, even those 100x larger 🤯

Open-sourcing the future of code reasoning! 🚀

Check it out https://huggingface.co/blog/open-r1/update-3
jjzha 
authored a paper 10 days ago
fdaudens 
posted an update 11 days ago
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5705
Honored to be named among their 12 pioneers and power players in the news industry in the 2025 Tech Trends Report from Future Today Strategy Group.

Incredible group to be part of - each person is doing groundbreaking work at the intersection of AI and journalism. Worth following them all: they're consistently sharing practical insights on building the future of news.

Take the time to read this report, it's packed with insights as always. The news & information section's #1 insight hits hard: "The most substantive economic impact of AI to date has been licensing payouts for a handful of big publishers. The competition will start shifting in the year ahead to separate AI 'haves' that have positioned themselves to grow from the 'have-nots.'"

This AI-driven divide is something I've been really concerned about. Now is the time to build more than ever!

👉 Full report here: https://ftsg.com/wp-content/uploads/2025/03/FTSG_2025_TR_FINAL_LINKED.pdf
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fdaudens 
posted an update 14 days ago
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AI will bring us "a country of yes-men on servers" instead of one of "Einsteins sitting in a data center" if we continue on current trends.

Must-read by @thomwolf deflating overblown AI promises and explaining what real scientific breakthroughs require.

https://thomwolf.io/blog/scientific-ai.html
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fdaudens 
posted an update 20 days 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
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jjzha 
authored a paper 20 days ago
fdaudens 
posted an update 22 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 24 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 27 days ago
frimelle 
authored 2 papers 28 days ago
frimelle 
posted an update 28 days ago
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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 2 papers 29 days ago