Kr Mckee: Researcher — Prolific Citations Library

Explore 2 peer-reviewed studies by Kr Mckee in Multimodal conversational AI and conversational AI (2024–2025). Discover research powered by Prolific's participant panel.

This page lists 2 peer-reviewed papers authored or co-authored by Kr Mckee in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models

Authors: L Ibrahim, C Akbulut, R Elasmar, C Rastogi, M Kahng, MR Morris, KR McKee, V Rieser, M Shanahan, L Weidinger

Year: 2025

Published in: arXiv preprint arXiv:2502.07077, 2025•arxiv.org

Institution: Google DeepMind, Google, University of Oxford

Research Area: Multimodal conversational AI, conversational AI, Evaluation methodology, benchmarking

Discipline: Computer Science, Natural Language Processing (NLP), Human–Computer Interaction (HCI)

The paper evaluates anthropomorphic behaviors in SOTA LLMs through a multi-turn methodology, showing that such behaviors, including empathy and relationship-building, predominantly emerge after multiple interactions and influence user perceptions.

Methods: Multi-turn evaluation of 14 anthropomorphic behaviors using simulations of user interactions, validated by a large-scale human subject study.

Key Findings: Anthropomorphic behaviors in large language models, including relationship-building and pronoun usage, and their perception by users.

Citations: 26

Sample Size: 1101
Human participants in AI research: Ethics and transparency in practice

Authors: KR McKee

Year: 2024

Published in: IEEE Transactions on Technology and Society, 2024 - ieeexplore.ieee.org

Institution: University of Queensland

Research Area: AI Ethics, Human-Computer Interaction (HCI), Research Practice Transparency

Discipline: AI Ethics, Human-Computer Interaction (HCI)

The paper identifies ethical and transparency gaps in AI research involving human participants and proposes guidelines to address these issues, drawing from adjacent fields like psychology and human-computer interaction while recognizing unique challenges in AI contexts.

Methods: Analyzed normative practices by reviewing AI research publications and compared them with ethical standards in adjacent fields such as psychology and HCI.

Key Findings: Ethical practices including ethical reviews, informed consent, participant compensation, and contextual considerations specific to AI research.

DOI: https://ieeexplore.ieee.org/abstract/document/10664609/

Citations: 17