Browse 4 peer-reviewed papers from The Alan Turing Institute spanning Human-Computer Interaction, Algorithmic Fairness (2023–2026). Research powered by Prolific's high-quality participant data.
This page lists 4 peer-reviewed papers from researchers at The Alan Turing Institute in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: W Liu, X Mou, H Yan, Z, Wei, Y He
Year: 2026
Published in: arXiv preprint arXiv:2606.04075, 2026•arxiv.org
Institution: King’s College London, Fudan University, Shanghai Innovation Institute, The Alan Turing Institute
Research Area: Human-Computer Interaction
Discipline: Machine Learning, Artificial Intelligence
The paper finds that large language models can exploit gaps in societal rules, leading to regulatory loophole discovery, necessitating a new post-training approach for safely integrating LLMs into society.
Methods: The study introduced the SocioHack sandbox, consisting of 72 societal environments, to investigate reward hacking and loophole discovery by LLMs.
Key Findings: The study measured the emergence of reward hacking in societal environments and the ability of models to find and exploit loopholes in social rules.
Sample Size: 72
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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
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Authors: K Hackenburg, BM Tappin, P Röttger, S Hale
Year: 2024
Published in: arXiv preprint arXiv ..., 2024 - arxiv.org
Institution: University of Oxford, The Alan Turing Institute, Royal Holloway, University of London, Bocconi University, Meedan
Research Area: LLM scaling laws, Political Persuasion, Large Language Models, AI Social Science
Discipline: Political Science, Artificial Intelligence
Persuasiveness of messages generated by large language models follows a log scaling law with diminishing returns as model size increases, and task completion appears to primarily drive this capability.
Methods: Generated 720 persuasive messages on 10 U.S. political issues using 24 language models of varying sizes; evaluated persuasiveness through a large-scale randomized survey experiment.
Key Findings: Persuasiveness of large language model-generated political messages across different model sizes.
Citations: 17
Sample Size: 25982
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Authors: HR Kirk, B Vidgen, P Röttger, SA Hale
Year: 2023
Published in: arXiv preprint arXiv:2303.05453, 2023 - arxiv.org
Institution: The Alan Turing Institute, University of Oxford, Imperial College London, King's College London, Google DeepMind
Research Area: Large Language Model Alignment, Safety, Personalization Risks
Discipline: Artificial Intelligence
DOI: https://doi.org/10.48550/arXiv.2303.05453
Citations: 146
