38 citations · 52 across the 14 of their papers we have counts for
15 papers
CorrectGuard: Eyes-Off Correctness Estimation for Black-Box Security Guardrails
Adam Faulkner, Nil-Jana Akpinar, Matthew Dressman
AI services increasingly rely on black-box security guardrails, yet privacy-preserving model auditing regimes often cannot measure how well these systems perform in both a human ey…
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…
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…
Precise Model Benchmarking with Only a Few Observations
Riccardo Fogliato, Pratik Patil, Nil-Jana Akpinar +1
How can we precisely estimate a large language model's (LLM) accuracy on questions belonging to a specific topic within a larger question-answering dataset? The standard direct est…
A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns
Vibhhu Sharma, Shantanu Gupta, Nil-Jana Akpinar +2
As recommender systems become widely deployed in different domains, they increasingly influence their users' beliefs and preferences. Auditing recommender systems is crucial as it…
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…
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…
The Impact of Differential Feature Under-reporting on Algorithmic Fairness
Nil-Jana Akpinar, Zachary C. Lipton, Alexandra Chouldechova
Predictive risk models in the public sector are commonly developed using administrative data that is more complete for subpopulations that more greatly rely on public services. In…
A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms
Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton +4
Motivated by the growing importance of reducing unfairness in ML predictions, Fair-ML researchers have presented an extensive suite of algorithmic 'fairness-enhancing' remedies. Mo…
Long-term Dynamics of Fairness Intervention in Connection Recommender Systems
Nil-Jana Akpinar, Cyrus DiCiccio, Preetam Nandy +1
Recommender system fairness has been studied from the perspectives of a variety of stakeholders including content producers, the content itself and recipients of recommendations. R…
The effect of differential victim crime reporting on predictive policing systems
Nil-Jana Akpinar, Maria De-Arteaga, Alexandra Chouldechova
Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are c…
Analyzing Student Strategies In Blended Courses Using Clickstream Data
Nil-Jana Akpinar, Aaditya Ramdas, Umut Acar
Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive op…
Sample Complexity Bounds for Recurrent Neural Networks with Application to Combinatorial Graph Problems
Nil-Jana Akpinar, Bernhard Kratzwald, Stefan Feuerriegel
Learning to predict solutions to real-valued combinatorial graph problems promises efficient approximations. As demonstrated based on the NP-hard edge clique cover number, recurren…