The Alan Turing Institute: Research Institution — Prolific Citations Library

Browse 3 peer-reviewed papers from The Alan Turing Institute spanning Algorithmic Fairness, Human Perception (2023–2025). Research powered by Prolific's high-quality participant data.

This page lists 3 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.

Papers (3 of 3)

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
Evidence of a log scaling law for political persuasion with large language models

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, LLM, 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
Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

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