Eth Z Rich: Research Institution — Prolific Citations Library

Browse 5 peer-reviewed papers from Eth Z Rich spanning Computation and Language, Algorithmic Fairness in Recruiting (2023–2025). Research powered by Prolific's high-quality participant data.

This page lists 5 peer-reviewed papers from researchers at Eth Z Rich in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (5 of 5)

Large Language Models Are More Persuasive Than Incentivized Human Persuaders

Authors: P. Schoenegger, F. Salvi, J. Liu, X. Nan, R. Debnath, B. Fasolo, E. Leivada, G. Recchia, F. Günther, A. Zarifhonarvar, J. Kwon, Z. Ul Islam, M. Dehnert, D. Y. H. Lee, M. G. Reinecke, D. G. Kamper, M. Kobaş, A. Sandford, J. Kgomo, L. Hewitt, S. Kapoor, K. Oktar, E. E. Kucuk, B. Feng, C. R. Jones, I. Gainsburg, S. Olschewski, N. Heinzelmann, F. Cruz, B. M. Tappin, T. Ma, P. S. Park, R. Onyonka, A. Hjorth, P. Slattery, Q. Zeng, L. Finke, I. Grossmann, A. Salatiello, E. Karger

Year: 2025

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

Institution: London School of Economics and Political Science, University of Cambridge, University College London, Massachusetts Institute of Technology, University of Oxford, Modulo Research, Stanford University, Federal Reserve Bank of Chicago, ETH Zürich, University of Johannesburg

Research Area: Computation and Language

Discipline: Social Science, Artificial Intelligence

This paper compares a frontier LLM (Claude Sonnet 3.5) against incentivized human persuaders in a conversational quiz setting, finding that the AI's persuasion capabilities surpass those of humans with real-money bonuses tied to performance.

Citations: 16
Perceived algorithmic fairness: An empirical study of transparency and anthropomorphism in algorithmic recruiting

Authors: J Ochmann, L Michels, V Tiefenbeck

Year: 2024

Published in: Information Systems ..., 2024 - Wiley Online Library

Institution: University of St. Gallen, Technische Universität München, ETH Zürich

Research Area: Algorithmic Fairness in Recruiting, Human-Algorithm Interaction, Transparency, Anthropomorphism.

Discipline: Information Systems

The study explores how transparency and anthropomorphism influence applicants' perceptions of algorithmic fairness in recruiting, revealing justice dimensions that shape these perceptions.

Methods: An online application scenario with eight experimental groups analyzing fairness perceptions using a stimulus-organism-response framework and organizational justice theory.

Key Findings: Perceptions of algorithmic fairness based on justice dimensions (procedural, distributive, interpersonal, and informational justice) and the impact of transparency and anthropomorphism interventions.

DOI: https://doi.org/10.1111/isj.12482

Citations: 65

Sample Size: 801
Whose preferences? differences in fairness preferences and their impact on the fairness of AI utilizing human feedback

Authors: M Lerner, F Dorner, E Ash, N Goel

Year: 2024

Published in: ... of the 62nd Annual Meeting of ..., 2024 - aclanthology.org

Institution: ETH Zürich, University of Oxford

Research Area: Fairness in AI, Content Moderation, Human-AI Alignment

Discipline: Computational Social Science

Citations: 5
Attributions toward artificial agents in a modified Moral Turing Test

Authors: Eyal Aharoni, Sharlene Fernandes, Daniel J. Brady, Caelan Alexander, Michael Criner, Kara Queen, Javier Rando, Eddy Nahmias, Victor Crespo

Year: 2024

Published in: Nature

Institution: Duke University, ETH Zurich, Georgia State University

Research Area: Moral Responsibility, Agency in AI, Human-AI Moral Interaction

Discipline: Artificial Intelligence Ethics
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