Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies

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Publication Details

Journal
EPJ Data Science
Published
2026-02-09
DOI
https://doi.org/10.1140/epjds/s13688-025-00612-3
Citations
1
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies

Evangelos Pournaras, Edith Elkind, Srijoni Majumdar
1 citations
EPJ Data Science
Ethics and Social Impacts of AI
article

Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies

Evangelos Pournaras, Edith Elkind, Srijoni Majumdar
article en
1 citations

Abstract

No abstract available for this paper.
EPJ Data ScienceVol. 15(1)
Northwestern University (US), University of Leeds (GB)
National Science Foundation, UK Research and Innovation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Peace, Justice and strong institutions
Openalex Percentile: Top 64%
Ethics and Social Impacts of AI
DOIOpen Access PDFOpenAlex
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Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies — Evangelos Pournaras, Edith Elkind, et al. · EPJ Data Science (2026) | TGRS Research Map | TGRS