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Towards a Cognitive Model for Inferring Dynamic Fairness Perception to Support Fairer Human-Robot Collaboration

Muneeb Ahmad Orcid Logo, Yosuke Fukuchi Orcid Logo

HRI Companion '26: Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, Pages: 272 - 276

Swansea University Author: Muneeb Ahmad Orcid Logo

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DOI (Published version): 10.1145/3776734.3794398

Abstract

Current research on measuring human perceptions of fairness in Human-Robot Teams (HRTs) has primarily focused on subjective metrics, such as rating statements either during or at the conclusion of interactions. This suggests a gap in examining the dynamic and evolving nature of fairness perceptions...

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Published in: HRI Companion '26: Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction
ISBN: 9798400723216
Published: New York, NY, USA Association for Computing Machinery (ACM) 2026
URI: https://cronfa.swan.ac.uk/Record/cronfa71477
first_indexed 2026-02-22T22:01:30Z
last_indexed 2026-03-13T05:25:00Z
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spelling v2 71477 2026-02-22 Towards a Cognitive Model for Inferring Dynamic Fairness Perception to Support Fairer Human-Robot Collaboration 9c42fd947397b1ad2bfa9107457974d5 0000-0001-8111-9967 Muneeb Ahmad Muneeb Ahmad true false 2026-02-22 MACS Current research on measuring human perceptions of fairness in Human-Robot Teams (HRTs) has primarily focused on subjective metrics, such as rating statements either during or at the conclusion of interactions. This suggests a gap in examining the dynamic and evolving nature of fairness perceptions objectively during human-robot collaboration. In this paper, we introduce a novel cognitive model that enables individuals to perceive fairness dynamically throughout an HRT experiment. This model is inspired by the Bayesian Theory of Mind, allowing us to infer perceptions of fairness in real-time. The core idea of the model is that fairness perception stems from a person's ongoing inference about the bias in a robot's value function. We establish an equation that translates this inference into a perceived fairness value, which is based not only on the inferred bias but also on the confidence of that inference. A qualitative comparison of the model's performance with a previous human-robot collaboration study suggests that it can effectively capture key trends in human fairness perception dynamically. These findings highlight the model's potential applicability, and it may be utilized in resource distribution algorithms in HRTs to promote fairer collaboration. Conference Paper/Proceeding/Abstract HRI Companion '26: Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction 272 276 Association for Computing Machinery (ACM) New York, NY, USA 9798400723216 Human-Robot Interaction, Fairness, Task or Resource Allocation, Bayesian Theory of Mind, Second-order Theory of Mind 16 3 2026 2026-03-16 10.1145/3776734.3794398 Short Paper COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University SU Library paid the OA fee (TA Institutional Deal) This work was supported in part by JSPS KAKENHI Grant Number JP24K20846. 2026-04-23T16:27:46.5748466 2026-02-22T20:44:14.0622114 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Muneeb Ahmad 0000-0001-8111-9967 1 Yosuke Fukuchi 0000-0002-7514-9040 2 71477__36578__1b96d40509f54b16910e62a88684da20.pdf 71477.VOR.pdf 2026-04-23T16:24:51.7138374 Output 941730 application/pdf Version of Record true © 2026 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. true eng https://creativecommons.org/licenses/by/4.0
title Towards a Cognitive Model for Inferring Dynamic Fairness Perception to Support Fairer Human-Robot Collaboration
spellingShingle Towards a Cognitive Model for Inferring Dynamic Fairness Perception to Support Fairer Human-Robot Collaboration
Muneeb Ahmad
title_short Towards a Cognitive Model for Inferring Dynamic Fairness Perception to Support Fairer Human-Robot Collaboration
title_full Towards a Cognitive Model for Inferring Dynamic Fairness Perception to Support Fairer Human-Robot Collaboration
title_fullStr Towards a Cognitive Model for Inferring Dynamic Fairness Perception to Support Fairer Human-Robot Collaboration
title_full_unstemmed Towards a Cognitive Model for Inferring Dynamic Fairness Perception to Support Fairer Human-Robot Collaboration
title_sort Towards a Cognitive Model for Inferring Dynamic Fairness Perception to Support Fairer Human-Robot Collaboration
author_id_str_mv 9c42fd947397b1ad2bfa9107457974d5
author_id_fullname_str_mv 9c42fd947397b1ad2bfa9107457974d5_***_Muneeb Ahmad
author Muneeb Ahmad
author2 Muneeb Ahmad
Yosuke Fukuchi
format Conference Paper/Proceeding/Abstract
container_title HRI Companion '26: Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction
container_start_page 272
publishDate 2026
institution Swansea University
isbn 9798400723216
doi_str_mv 10.1145/3776734.3794398
publisher Association for Computing Machinery (ACM)
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hierarchy_parent_id facultyofscienceandengineering
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department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Current research on measuring human perceptions of fairness in Human-Robot Teams (HRTs) has primarily focused on subjective metrics, such as rating statements either during or at the conclusion of interactions. This suggests a gap in examining the dynamic and evolving nature of fairness perceptions objectively during human-robot collaboration. In this paper, we introduce a novel cognitive model that enables individuals to perceive fairness dynamically throughout an HRT experiment. This model is inspired by the Bayesian Theory of Mind, allowing us to infer perceptions of fairness in real-time. The core idea of the model is that fairness perception stems from a person's ongoing inference about the bias in a robot's value function. We establish an equation that translates this inference into a perceived fairness value, which is based not only on the inferred bias but also on the confidence of that inference. A qualitative comparison of the model's performance with a previous human-robot collaboration study suggests that it can effectively capture key trends in human fairness perception dynamically. These findings highlight the model's potential applicability, and it may be utilized in resource distribution algorithms in HRTs to promote fairer collaboration.
published_date 2026-03-16T16:27:50Z
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