Isabelle Augenstein's picture

Isabelle Augenstein

IAugenstein

·

http://isabelleaugenstein.github.io/

AI & ML interests

Low-resource learning, natural language understanding, fact checking, explainable AI

Recent Activity

authored a paper about 1 hour ago

Unstructured Evidence Attribution for Long Context Query Focused Summarization

reacted to frimelle's post with ❤️ 1 day ago

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: https://huggingface.co/papers/2502.11995

reacted to frimelle's post with 🔥 1 day ago

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: https://huggingface.co/papers/2502.11995

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Organizations

Papers 26

arxiv:2502.14409

arxiv:2502.11995

arxiv:2502.09083

arxiv:2412.17031

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