Jimin Mun

We find that 1) acceptance of AI varies widely depending on use case context, 2) judgments differ between demographic groups, and 3) people use both cost-benefit AND rule-based reasoning to make their decisions where diverging strategies show higher disagreement.

To build consensus around AI use cases, it's imperative to understand how people, especially lay-users, reason about AI use cases. We asked 197 participants to make decisions on individual AI use cases and share their reasoning process.

Why (not) use AI? Analyzing People's Reasoning and Conditions for AI Acceptability In recent years, there has been a growing recognition of the need to incorporate lay-people's input into the governance and acceptability assessment of AI usage. However, how and why people judge acce...

Next stop for conference hopping: #AIES2025 in Madrid!

I'll be giving an oral presentation of our paper Why (Not) Use AI during paper session 1 tomorrow (10/20) at 11:45AM :)

See details in thread below 👇

arxiv.org/abs/2502.07287

Our tool Riveter💪 used for a creative and interesting study of fan fiction! Riveter helps you work with “connotation frames” (verb lexica) to measure biases in your dataset. @julianeugarten.bsky.social’s overview and explanations are really clear, highly recommend!

github.com/maartensap/r...

Proud to see my article 'Using Riveter to map gendered power dynamics in Hades/Persephone fan fiction' in @journal.transformativeworks.org, my favorite academic journal.

Want to know how fanfiction portrays power dynamics between these two? Read on!

journal.transformativeworks.org/index.php/tw...

🔈For the SoLaR workshop
@COLM_conf
we are soliciting opinion abstracts to encourage new perspectives and opinions on responsible language modeling, 1-2 of which will be selected to be presented at the workshop.

Please use the google form below to submit your opinion abstract ⬇️

We are accepting papers for the following two tracks!

🤖 ML track: algorithms, math, computation
📚 Socio-technical track: policy, ethics, human participant research

Third Workshop on Socially Responsible Language Modelling Research (SoLaR) 2025 COLM 2025 in-person Workshop, October 10th at the Palais des Congrès in Montreal, Canada

Interested in shaping the progress of safe AI and meeting leading researchers in the field? SoLaR@COLM 2025 is looking for paper submissions / reviewers!

Submit your paper / sign up to review by June 23

CFP and workshop info: solar-colm.github.io
Reviewer sign up: docs.google.com/forms/d/e/1F...

How does the public conceptualize AI? Rather than self-reported measures, we use metaphors to understand the nuance and complexity of people’s mental models. In our #FAccT2025 paper, we analyzed 12,000 metaphors collected over 12 months to track shifts in public perceptions.

Life update! Excited to announce that I’ll be starting as an assistant professor at Cornell Info Sci in August 2026! I’ll be recruiting students this upcoming cycle!

An abundance of thanks to all my mentors and friends who helped make this possible!!

Website: solar-colm.github.io
With:
@usmananwar.bsky.social @liweijiang.bsky.social @valentinapy.bsky.social @sharonlevy.bsky.social Daniel Tan @akhilayerukola.bsky.social @jiminmun.bsky.social Ruth Appel @sumeetrm.bsky.social @davidskrueger.bsky.social Sheila McIlraith @maartensap.bsky.social

📢 The SoLaR workshop will be collocated with COLM!
@colmweb.org

SoLaR is a collaborative forum for researchers working on responsible development, deployment and use of language models.

We welcome both technical and sociotechnical submissions, deadline July 5th!

Check out our work on improving LLM's ability to seek information through asking better questions! 💫