ORCID
- Musfira Jilani: 0000-0001-9632-957X
Abstract
Recent years have seen a growing interest in industrial and social robots; however, their widespread adoption remains constrained due to a limited understanding of trust in human-robot interactions (HRI). Existing approaches to assessing trust in human-robot interactions primarily rely on post-hoc measurements, often collected through self-reports, which are not very effective and do not allow for real-time decision making, which is one of the main aspects of human-robot interactions. This research proposes a data-driven approach to measuring trust in HRI.
Keywords
explainable AI, human-robot interactions, LSTM, SHAP, trust assessment
DOI Link
Publication Date
2026-02-16
Event
3rd International Conference on Human-Centred AI - Education and Practice, HCAI-ep 2026
Publication Title
HCAI-ep 2026 - Proceedings of the 2026 Conference on Human Centered Artificial Intelligence - Education and Practice
Publisher
Association for Computing Machinery (ACM)
ISBN
9798400721533
First Page
122
Last Page
122
Deposit Date
2026-05-14
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Hubay, Csenge and Jilani, Musfira, "An Explainable ML Approach to Modeling Trust in HRI" (2026). Research Outputs: 2025-Present. 6.
https://arrow.tudublin.ie/faccomentro/6