1 citations · 1 across the 2 of their papers we have counts for
9 papers
LLM or Human? Perceptions of Trust and Information Quality in Research Summaries
Nil-Jana Akpinar, Sandeep Avula, CJ Lee +3
Large Language Models (LLMs) are increasingly used to generate and edit scientific abstracts, yet their integration into academic writing raises questions about trust, quality, and…
Users Mispredict Their Own Preferences for AI Writing Assistance
Vivian Lai, Zana Buçinca, Nil-Jana Akpinar +5
Proactive AI writing assistants need to predict when users want drafting help, yet we lack empirical understanding of what drives preferences. Through a factorial vignette study wi…
Who's Asking? Evaluating LLM Robustness to Inquiry Personas in Factual Question Answering
Nil-Jana Akpinar, Chia-Jung Lee, Vanessa Murdock +1
Large Language Models (LLMs) should answer factual questions truthfully, grounded in objective knowledge, regardless of user context such as self-disclosed personal information, or…
Local Causal Discovery for Structural Evidence of Direct Discrimination
Jacqueline Maasch, Kyra Gan, Violet Chen +3
Identifying the causal pathways of unfairness is a critical objective for improving policy design and algorithmic decision-making. Prior work in causal fairness analysis often requ…
Improving LLM Group Fairness on Tabular Data via In-Context Learning
Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar +4
Large language models (LLMs) have been shown to be effective on tabular prediction tasks in the low-data regime, leveraging their internal knowledge and ability to learn from instr…
Authenticity and exclusion: social media algorithms and the dynamics of belonging in epistemic communities
Nil-Jana Akpinar, Sina Fazelpour
Recent philosophical work has explored how the social identity of knowers influences how their contributions are received, assessed, and credited. However, a critical gap remains r…