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Lucie-Aimée Kaffee

frimelle

https://luciekaffee.github.io/

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published an article about 4 hours ago

🇪🇺 EU AI Act: Comments on the Third Code of Practice Draft 🇪🇺

authored a paper 21 days ago

Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions

authored a paper 21 days ago

Presumed Cultural Identity: How Names Shape LLM Responses

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frimelle's activity

published an article about 4 hours ago

Article

🇪🇺 EU AI Act: Comments on the Third Code of Practice Draft 🇪🇺

and 1 other •

about 4 hours ago

• 4

authored 2 papers 21 days ago

Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions

Paper • 2310.05779 • Published Oct 9, 2023 • 1

Presumed Cultural Identity: How Names Shape LLM Responses

Paper • 2502.11995 • Published 24 days ago • 10

replied to their post 21 days ago

Haven't forgotten your comment, actually really appreciate it! I am trying to write a blogpost on this at the moment, but in the meantime, there's a whole range of Hugging Face courses that I think are an excellent start to get more into the tech side of AI, and get you started on playing with models and training datasets: https://huggingface.co/learn Hope it helps, excited to see what you build.

posted an update 21 days ago

Post

2381

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)

upvoted a paper 21 days ago

Presumed Cultural Identity: How Names Shape LLM Responses

Paper • 2502.11995 • Published 24 days ago • 10

posted an update about 1 month ago

Post

524

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.

posted an update about 1 month ago

Post

1679

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.