tomorrow 6/20, i'm presenting this paper at #alt_FAccT, a NYC local meeting for @FAccTConference ✨🎤 paper session #3 🎤✨ 🗽1:30pm June 20, Fri @ MSR NYC🗽 ⬇️ our #FAccT2025 paper is abt “what if ur ChatGPT spoke queer slang and AAVE?” 📚🔗 bit.ly/not-like-us

What if AI played the role of your sassy gay bestie 🏳️‍🌈 or AAVE-speaking friend 👋🏾? You: “Can you plan a trip?” 🤖 AI: “Yasss queen! let’s werk this babe✨💅” LLMs can talk like us, but it shapes how we trust, rely on & relate to them 🧵 📣 our #FAccT2025 paper: bit.ly/3HJ6rWI [1/9]

🖋️ Curious how writing differs across (research) cultures? 🚩 Tired of “cultural” evals that don't consult people? We engaged with interdisciplinary researchers to identify & measure ✨cultural norms✨in scientific writing, and show that❗LLMs flatten them❗ 📜 arxiv.org/abs/2506.00784 [1/11]

🏳️‍🌈🎨💻📢 Happy to share our workshop study on queer artists’ experiences critically engaging with GenAI Looking forward to presenting this work at #FAccT2025 and you can read a pre-print here: arxiv.org/abs/2503.09805

RLHF is built upon some quite oversimplistic assumptions, i.e., that preferences between pairs of text are purely about quality. But this is an inherently subjective task (not unlike toxicity annotation) -- so we wanted to know, do biases similar to toxicity annotation emerge in reward models?

Reward models for LMs are meant to align outputs with human preferences—but do they accidentally encode dialect biases? 🤔 Excited to share our paper on biases against African American Language in reward models, accepted to #NAACL2025 Findings! 🎉 Paper: arxiv.org/abs/2502.12858 (1/10)

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