Hr Kirk: Researcher — Prolific Citations Library

Explore 5 peer-reviewed studies by Hr Kirk in Experimental evaluation and RCT (2023–2025). Discover research powered by Prolific's participant panel.

This page lists 5 peer-reviewed papers authored or co-authored by Hr Kirk in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (5 of 5)

Conversational AI increases political knowledge as effectively as self-directed internet search

Authors: L Luettgau, HR Kirk, K Hackenburg, J Bergs, H Davidson, H Ogden, D Siddarth, S Huang

Year: 2025

Published in: ARXIV

Institution: AI Security Institute, I Policy Directorate, Collective Intelligence Project, Anthropic

Research Area: Experimental evaluation, RCT, Survey Research

Discipline: Computer Science, Human–Computer Interaction (HCI)

Conversational AI is as effective as self-directed internet searches in increasing political knowledge, reducing misinformation beliefs, and promoting accuracy among users in the UK during the 2024 election period.

Methods: A national survey (N=2,499) measured conversational AI usage for political information-seeking, followed by a series of randomised controlled trials (N=2,858) comparing conversational AI to self-directed internet search in improving political knowledge.

Key Findings: Extent of conversational AI usage for political knowledge-seeking in the UK and its efficacy in enhancing political knowledge and reducing misinformation compared to traditional internet searches.

Citations: 3

Sample Size: 5357
The PRISM alignment dataset: What participatory, representative and individualised human feedback reveals about the subjective and multicultural alignment of large ...

Authors: HR Kirk, M Bartolo, A Whitefield, P Rottger

Year: 2024

Published in: Advances in ..., 2024 - proceedings.neurips.cc

Institution: Meta, Cohere, AWS AI Labs, Contextual AI, Factored AI, University of Oxford, Bocconi University, Meedan, Hugging Face, University College London, ML Commons, University of Pennsylvania

Research Area: LLM Alignment, Human Feedback, Multicultural Studies

Discipline: Artificial Intelligence, Computational Social Science

The PRISM Alignment Dataset presents a large-scale, culturally diverse human feedback dataset linking sociodemographic profiles of 1,500 participants from 75 countries to their contextual preferences and fine‑grained ratings in 8,011 live conversations with 21 LLMs. This enables analysis of how subjective values vary across people and cultures in LLM alignment data.

DOI: https://doi.org/10.52202/079017-3342

Citations: 204
Why human-AI relationships need socioaffective alignment

Authors: HR Kirk, I Gabriel, C Summerfield, B Vidgen

Year: 2024

Published in: Humanities and Social ..., 2025 - nature.com

Institution: Oxford Internet Institute, University of Oxford

Research Area: Socioaffective Alignment in Human-AI Relationships, AI Ethics, Behavioral Science

Discipline: Artificial Intelligence, Behavioral Science

The paper emphasizes the need for socioaffective alignment in human-AI relationships to ensure AI systems support human psychological needs rather than exploit them, as interactions with AI transition from transactional to sustained engagement.

Methods: Conceptual analysis of socioaffective dynamics in human-AI interactions, framed through psychological theories and principles.

Key Findings: Exploration of how AI systems impact socioaffective relationships, psychological needs, autonomy, companionship, and human well-being.

DOI: https://doi.org/10.1057/s41599-025-04532-5

Citations: 59
The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub

Authors: HR Kirk, C Osborne

Year: 2024

Published in: ArXiv

Institution: Alan Turing Institute, Oxford Internet Institute, Oxford University

Research Area: Computational Social Science, AI Community Analysis, Hugging Face Hub Activity

Discipline: Computational Social Science
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