University Of Lausanne: Research Institution — Prolific Citations Library

Browse 8 peer-reviewed papers from University Of Lausanne spanning Algorithmic Fairness, Human Perception (2023–2025). Research powered by Prolific's high-quality participant data.

This page lists 8 peer-reviewed papers from researchers at University Of Lausanne in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (8 of 8)

Dimensions of diversity in human perceptions of algorithmic fairness

Authors: N Grgić-Hlača, G Lima, A Weller

Year: 2025

Published in: Proceedings of the 2nd ..., 2022 - dl.acm.org

Institution: Max Planck Institute, École Polytechnique Fédérale de Lausanne, University of Cambridge, The Alan Turing Institute

Research Area: Algorithmic Fairness, Human Perception, Diversity in AI Decision-Making

Discipline: Social Science, Artificial Intelligence

This study examines how sociodemographic factors and personal experience influence perceptions of fairness in algorithmic decision-making, particularly in bail decisions, highlighting the importance of diverse perspectives in regulatory oversight.

Methods: Explored perceptions of procedural fairness using surveys to assess the influence of demographics and personal experiences.

Key Findings: Impact of demographics (age, education, gender, race, political views) and personal experience on perceptions of fairness of algorithmic feature use in bail decisions.

DOI: 10.1145/3551624.3555306

Citations: 62
A meta-analysis of the persuasive power of large language models

Authors: L Hölbling, S Maier, S Feuerriegel

Year: 2025

Published in: Scientific Reports, 2025 - nature.com

Institution: University of Lausanne, University of Zurich, University of St. Gallen

Research Area: LLMs in Persuasion, Meta-Analysis, Artificial Intelligence, Human-Computer Interaction (HCI)

Discipline: Artificial Intelligence

Large language models (LLMs) demonstrate similar persuasive performance to humans overall, but their effectiveness varies widely based on contextual factors such as model type, conversation design, and domain.

Methods: Systematic review and meta-analysis using Hedges' g to compute standardized effect sizes, with exploratory moderator analyses and publication bias checks (Egger's test, trim-and-fill analysis).

Key Findings: The persuasive effectiveness of LLMs compared to humans across various contexts and studies.

Sample Size: 17422
RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting

Authors: Pooja S. B. Rao, Sanja Šćepanović, Ke Zhou, Edyta Paulina Bogucka, D Quercia

Year: 2025

Published in: ArXiv

Institution: Nokia Bell Labs, University of Lausanne

Research Area: AI Risk Management, Model Risk Reporting, RAG Pipeline, RAG

Discipline: Artificial Intelligence

RiskRAG improves AI model risk reporting by offering pre-populated, contextualized risk reports that are preferred by developers, designers, and media professionals over standard model cards.

Methods: Developed a Retrieval Augmented Generation system based on five design requirements co-created with 16 developers, using a dataset of 450K model cards and 600 real-world incidents. Evaluated RiskRAG in preliminary and final studies with a total of 125 participants.

Key Findings: Effectiveness of RiskRAG in improving risk reporting and decision-making compared to standard model cards.

Sample Size: 125
Employees adhere more to unethical instructions from human than AI supervisors: Complementing experimental evidence with machine learning

Authors: L Lanz, R Briker, FH Gerpott

Year: 2024

Published in: Journal of Business Ethics, 2024 - Springer

Institution: University of Lausanne, University of Neuchâtel, University of Bern

Research Area: AI Ethics, Organizational Behavior, Supervisory Influence in the Workplace

Discipline: Business Ethics, Organizational Behavior, Artificial Intelligence Ethics

Employees are less likely to adhere to unethical instructions from AI supervisors compared to human supervisors, partly due to perceived differences in 'mind' and individual characteristics like compliance tendency and age.

Methods: The study employed four experiments using causal forest and transformer-based machine learning algorithms, as well as pre-registered experimental manipulations to evaluate employee behavior towards unethical instructions from AI and human supervisors.

Key Findings: Adherence to unethical instructions from AI versus human supervisors; mediating role of perceived mind and moderating factors like compliance tendency and age.

DOI: https://doi.org/10.1007/s10551-023-05393-1

Citations: 72

Sample Size: 1701
Detecting the corruption of online questionnaires by artificial intelligence

Authors: B Lebrun, S Temtsin, A Vonasch

Year: 2024

Published in: Frontiers in Robotics and ..., 2024 - frontiersin.org

Institution: University of Lausanne, University of California Berkeley, University of Massachusetts Amherst, Arizona State University

Research Area: AI in Social Science Research, Survey Methodology, Data Quality

Discipline: Artificial Intelligence

The study examines the integrity of online questionnaire responses and concludes that humans can identify AI-generated text with 76% accuracy, but current AI detection systems are ineffective, raising concerns about data quality in online surveys.

Methods: Human participants and automatic AI detection systems were tested on their ability to differentiate AI-generated text from human-generated text in the context of online questionnaires.

Key Findings: The study measured the ability of humans and AI detection tools to correctly identify whether text was generated by a human or an AI system for online questionnaire responses.

DOI: https://doi.org/10.3389/frobt.2023.1277635

Citations: 26
On the role of large language models in crowdsourcing misinformation assessment

Authors: J Xu, L Han, S Sadiq, G Demartini

Year: 2024

Published in: Proceedings of the International ..., 2024 - ojs.aaai.org

Institution: University of Lausanne, EPFL, University of Southampton, University of Queensland

Research Area: Crowdsourcing, Misinformation Assessment, LLM

Discipline: Artificial Intelligence

DOI: https://doi.org/10.1609/icwsm.v18i1.31417

Citations: 6
Evaluating Creative Short Story Generation in Humans and Large Language Models

Authors: Mete Ismayilzada1,2, Claire Stevenson3, Lonneke van der Plas

Year: 2024

Published in: ArXiv

Institution: Idiap Research Institute, University of Amsterdam, Università della Svizzera Italiana, École Polytechnique Fédérale de Lausanne

Research Area: Creative Story Generation, LLM Evaluation, Computational Creativity

Discipline: Artificial Intelligence, Natural Language Processing, Computational Creativity
From lab-testing to web-testing in cognitive research: Who you test is more important than how you test

Authors: K Uittenhove, S Jeanneret, E Vergauwe

Year: 2023

Published in: Journal of Cognition, 2023 - pmc.ncbi.nlm.nih.gov

Institution: University of Lausanne, University of Geneva, EPFL, University of Neuchâtel, NiH

Research Area: Cognitive Psychology, Research Methodology, Behavioral Research Methods, Web-based Behavioral Research

Discipline: Cognitive Research, Psychology

Citations: 83