Browse 5 peer-reviewed papers from Eth Z Rich spanning Natural Language Processing, 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.
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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: Natural Language Processing
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
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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
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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
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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: AI Ethics
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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
