Cornell: Research Institution — Prolific Citations Library

Browse 7 peer-reviewed papers from Cornell spanning Human-Computer Interaction (HCI), Reinforcement Learning from Human Feedback (RLHF) (2020–2025). Research powered by Prolific's high-quality participant data.

This page lists 7 peer-reviewed papers from researchers at Cornell in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (7 of 7)

Lay Perceptions of Algorithmic Discrimination in the Context of Systemic Injustice

Authors: G Lima, N Grgić-Hlača, M Langer, Y Zou

Year: 2025

Published in: Proceedings of the 2025 CHI ..., 2025 - dl.acm.org

Institution: University of Maryland, Max Planck Institute, Stanford University, Cornell University

Research Area: Algorithmic Fairness, Systemic Injustice, Social Perception of AI, Algorithmic Discrimination

Discipline: Computational Social Science

The study examines how contextualizing algorithms within systemic injustice impacts perceptions of algorithmic discrimination, finding disparate effects based on participant group identity and revealing unintended consequences of such contextualization.

Methods: 2x3 between-participants experiment using the hiring context as a case-study; examined the influence of systemic injustice information and algorithms' bias perpetuation on lay perceptions.

Key Findings: Impact of systemic injustice framing and explanation of algorithmic bias perpetuation on participants' views of algorithmic fairness and discrimination.

DOI: 10.1145/3706598.3713536

Citations: 2

Sample Size: 716
Benchmarking World-Model Learning

Authors: A Warrier, D Nguyen, M Naim, M Jain, Y Liang, K Schroeder, C Yang, JB Tenenbaum, S Vollmer, K Ellis, Z Tavares

Year: 2025

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

Institution: Basis Research Institute, DFKI GmbH, Harvard University, Quebec AI Institute, University of Cambridge, Massachusetts Institute of Technology, Cornell University

Research Area: Agent learning, World Models, Benchmarking, Evaluation protocols, RLHF, LLM

Discipline: Computer Science, Artificial Intelligence, Machine Learning

The paper introduces WorldTest, a novel protocol for evaluating model-learning agents using reward-free exploration and behavior-based scoring, and demonstrates that humans outperform models on the AutumnBench suite of tasks, revealing significant gaps in world-model learning.

Methods: The authors proposed WorldTest, a protocol separating reward-free interaction from scored tests in related environments, with evaluations done using AutumnBench—a dataset of 43 grid-world environments and 129 tasks across prediction, planning, and causal dynamics.

Key Findings: Performance of model-learning agents and humans in acquiring world models for masked-frame prediction, planning, and understanding causal dynamics.

Citations: 1

Sample Size: 517
Examining Human-AI Collaboration for Co-Writing Constructive Comments Online

Authors: FARHANA SHAHID, MAXIMILIAN DITTGEN

Year: 2024

Published in: ArXiv

Institution: Cornell University

Research Area: Human-AI Collaboration, Constructive Discourse, Online Communication, Human-Computer Interaction (HCI)

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence
Open problems and fundamental limitations of reinforcement learning from human feedback

Authors: S Casper, X Davies, C Shi, TK Gilbert

Year: 2023

Published in: arXiv preprint arXiv ..., 2023 - arxiv.org

Institution: Columbia University, Cornell Tech, Apollo Research, ETH Zurich, UC Berkeley, University of Sussex, Independent

Research Area: Reinforcement Learning from Human Feedback (RLHF), Alignment, LLM Limitations

Discipline: Artificial Intelligence

DOI: https://doi.org/10.48550/arXiv.2307.15217

Citations: 848
Safe RLHF: Safe reinforcement learning from human feedback

Authors: J Dai, X Pan, R Sun, J Ji, X Xu, M Liu, Y Wang

Year: 2023

Published in: arXiv preprint arXiv ..., 2023 - arxiv.org

Institution: Cornell University, Georgia Institute of Technology

Research Area: Reinforcement Learning from Human Feedback (RLHF), Safe AI, Reinforcement Learning

Discipline: Artificial Intelligence, Machine Learning

DOI: https://doi.org/10.48550/arXiv.2310.12773

Citations: 598
The trajectory of truth: A longitudinal study of the illusory truth effect

Authors: EL Henderson, DJ Simons, DJ Barr

Year: 2021

Published in: Journal of cognition, 2021 - pmc.ncbi.nlm.nih.gov

Institution: University of Illinois at Urbana-Champaign, Cornell University, University of Rochester, NiH

Research Area: Cognitive Psychology, Illusory Truth Effect, Misinformation, Memory

Discipline: Psychology

Citations: 78
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