University Of New York: Research Institution — Prolific Citations Library

Browse 12 peer-reviewed papers from University Of New York spanning Human-Computer Interaction (HCI), Disclosure psychology (2020–2026). Research powered by Prolific's high-quality participant data.

This page lists 12 peer-reviewed papers from researchers at University Of New York in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (12 of 12)

The Artificial Intelligence Disclosure Penalty: Humans Persistently Devalue AI-Generated Creative Writing

Authors: M Raj, JM Berg, R Seamans

Year: 2026

Published in: Journal of Experimental Psychology …, 2026 - psycnet.apa.org

Institution: New York University, University of Michigan, Wharton

Research Area: Disclosure psychology, Biases in human–machine evaluation, AI Biases

Discipline: Experimental psychology

This paper sits at the intersection of experimental psychology, social cognition, and consumer judgment, examining how AI disclosure triggers persistent authenticity-based bias against creative work, revealing a robust form of algorithmic aversion in symbolic and expressive domains.

DOI: https://doi.org/10.1037/xge0001889
Impact of annotator demographics on sentiment dataset labeling

Authors: Y Ding, J You, TK Machulla, J Jacobs, P Sen

Year: 2025

Published in: Proceedings of the ..., 2022 - dl.acm.org

Institution: University of California Irvine, University of Florida, State University of New York at Buffalo, University of Waterloo, Virginia Tech

Research Area: Computational Social Science, Human-Computer Interaction (HCI), Sentiment Analysis

Discipline: Computational Social Science

Demographic differences among annotators significantly affect sentiment dataset labels, causing up to a 4.5% accuracy difference in sentiment prediction models.

Methods: Crowdsourced annotations from >1000 workers combined with demographic data; analysis of multimodal sentiment datasets and evaluation using machine learning models.

Key Findings: Impact of annotator demographics on sentiment labeling and its effect on model predictions.

DOI: https://doi.org/10.1145/3555632

Citations: 28

Sample Size: 1000
Laypeople's use of and attitudes toward large language models and search engines for health queries: survey study

Authors: T Mendel, N Singh, DM Mann, B Wiesenfeld

Year: 2025

Published in: Journal of medical ..., 2025 - jmir.org

Institution: The City University of New York, George Washington University, New York University

Research Area: LLMs in Digital Health, Health Queries, User Attitudes

Discipline: Digital Health

Laypeople primarily use search engines over large language models (LLMs) for health queries, perceiving LLMs as less useful but less biased and more human-like while exhibiting no significant difference in trust or ease of use.

Methods: A screening survey followed by logistic regression analysis and a follow-up survey; comparisons were performed using ANOVA, Tukey post hoc tests, and paired-sample Wilcoxon tests.

Key Findings: Demographics and behaviors of LLM and search engine users for health queries, perceived usefulness, ease of use, trustworthiness, bias, and anthropomorphism.

Citations: 21

Sample Size: 2002
Large Language Models are often politically extreme, usually ideologically inconsistent, and persuasive even in informational contexts

Authors: N Aldahoul, H Ibrahim, M Varvello, A Kaufman

Year: 2025

Published in: arXiv preprint arXiv ..., 2025 - arxiv.org

Institution: Delft University of Technology, University of Pennsylvania, New York University, King Abdullah University of Science and Technology, Massachusetts Institute of Technology, University of Texas at Austin

Research Area: Artificial Intelligence, Computers and Society, Political Science

Discipline: Artificial Intelligence, Social Science

The study finds that Large Language Models (LLMs) exhibit extreme political views on specific topics despite appearing ideologically moderate overall, and demonstrate a persuasive influence on users' political preferences even in informational contexts.

Methods: Compared 31 LLMs' political biases against benchmarks (legislators, judges, representative voter samples) and conducted a randomized experiment to measure their persuasive impact in informational interactions.

Key Findings: Ideological consistency, political extremity, and persuasive effects of LLMs in information-seeking contexts.

Citations: 7

Sample Size: 31
Beyond Face Value: Visual and Auditory Signals in Human and Machine Trust Judgments

Authors: N Tyulina, Y Yu, TA Emmanouil, SI Levitan

Year: 2025

Published in: Proceedings of the 7th ACM ..., 2025 - dl.acm.org

Institution: University of Cambridge, University of Bath, University of Edinburgh, New York University

Research Area: Human-AI Interaction, Trust and Perception, Nonverbal Communication

Discipline: Applied Linguistics

Trust judgments are primarily influenced by auditory cues in both humans and multimodal models, though subtle differences in modality weighting exist between them.

Methods: Behavioral experiment with trust ratings of bimodal stimuli across four trust congruence conditions, combined with a multimodal model trained using HuBERT and ResNet-50 with late fusion, analyzed using Permutation Feature Importance (PFI).

Key Findings: The construction of trust from visual and auditory signals in both humans and multimodal models, focusing on modality dominance and feature weighting.

Sample Size: 150
Large language models must be taught to know what they don't know

Authors: S Kapoor, N Gruver, M Roberts

Year: 2024

Published in: Advances in ..., 2024 - proceedings.neurips.cc

Institution: Abacus AI, University of Cambridge, New York University, Columbia University

Research Area: Uncertainty Estimation, LLM Limitations, Know-What-You-Don't-Know, Computational Cognition

Discipline: Artificial Intelligence

Fine-tuning large language models (LLMs) on a small dataset of graded examples improves uncertainty estimations, enhancing their applicability in high-stakes scenarios and human-AI collaboration.

Methods: The researchers fine-tuned LLMs using a small dataset of graded correct and incorrect answers with LoRA (Low-Rank Adaptation) to create uncertainty estimates and conducted a user study to investigate their utility in human-AI collaboration.

Key Findings: Calibration and generalization of uncertainty estimates, performance of fine-tuning LLMs for uncertainty estimation, and human-AI interaction improvements informed by uncertainty data.

Citations: 71

Sample Size: 1000
Predicting results of social science experiments using large language models

Authors: L Hewitt, A Ashokkumar, I Ghezae, R Willer

Year: 2024

Published in: Preprint, 2024 - samim.io

Institution: Stanford University, New York University

Research Area: Social Science Experiments, Large Language Model Prediction, LLM

Discipline: Computational Social Science

The study presents a framework using large language models to predict outcomes of social science field experiments, achieving 78% accuracy but facing challenges with experiments on complex social issues.

Methods: Authors used an automated framework powered by large language models to predict outcomes of 276 field experiments drawn from economics literature.

Key Findings: The prediction accuracy of large language models for outcomes of field experiments addressing various human behaviors.

Citations: 68

Sample Size: 276
Cue to Trust? Investigating the Impact of Political Advertising Transparency Disclaimers on Citizen Trust Evaluations

Authors: HC Gordon, T Stafford, K Dommett

Year: 2024

Published in: ... of the Annual Meeting of the ..., 2024 - escholarship.org

Institution: University of California, Irvine, University of New York, Buffalo, University of Bath

Research Area: Political Advertising, Trust, Political Communication, Transparency

Discipline: Political Science, Communication
The “Naturalistic Free Recall” dataset: four stories, hundreds of participants, and high-fidelity transcriptions

Authors: O Raccah, P Chen, TM Gureckis, D Poeppe, VA Vo

Year: 2024

Published in: Nature

Institution: Intel Labs, New York University, Yale University

Research Area: Cognitive Psychology, Memory Research, Natural Language Processing (NLP)

Discipline: Psychology, Artificial Intelligence
People think that social media platforms do (but should not) amplify divisive content

Authors: S Rathje, C Robertson, WJ Brady

Year: 2023

Published in: Perspectives on ..., 2024 - journals.sagepub.com

Institution: New York University, University of North Carolina at Chapel Hill

Research Area: Social Media Perception, Divisive Content, Platform Amplification

Discipline: Social Science

Citations: 82
Creativity on paid crowdsourcing platforms

Authors: J Oppenlaender, K Milland, A Visuri, P Ipeirotis

Year: 2020

Published in: Proceedings of the ..., 2020 - dl.acm.org

Institution: Cornell, Aalto University, New York University

Research Area: Crowdsourcing, Creativity, Human-Computer Interaction (HCI)

Discipline: Human-Computer Interaction (HCI)

Citations: 66
Survey curious? start-up guide and best practices for running surveys and experiments online

Authors: A Bergman, A Chinco, SM Hartzmark

Year: 2020

Published in: Start-Up Guide and ..., 2020 - papers.ssrn.com

Institution: Yale School of Management, University of Miami School of Business, New York University, Columbia University

Research Area: Survey Methodology, Experimental Design, Social Science Research Methods

Discipline: Social Science Research Methods

Citations: 34