The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems
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Abstract (arXiv)
The trustworthiness of Robots and Autonomous Systems (RAS) has gained a prominent position on many research agendas towards fully autonomous systems. This research systematically explores, for the first time, the key facets of human-centered AI (HAI) for trustworthy RAS. In this article, five key properties of a trustworthy RAS initially have been identified. RAS must be (i) safe in any uncertain and dynamic surrounding environments; (ii) secure, thus protecting itself from any cyber-threats; (iii) healthy with fault tolerance; (iv) trusted and easy to use to allow effective human-machine interaction (HMI), and (v) compliant with the law and ethical expectations. Then, the challenges in implementing trustworthy autonomous system are analytically reviewed, in respects of the five key properties, and the roles of AI technologies have been explored to ensure the trustiness of RAS with respects to safety, security, health and HMI, while reflecting the requirements of ethics in the design of RAS. While applications of RAS have mainly focused on performance and productivity, the risks posed by advanced AI in RAS have not received sufficient scientific attention. Hence, a new acceptance model of RAS is provided, as a framework for requirements to human-centered AI and for implementing trustworthy RAS by design. This approach promotes human-level intelligence to augment human's capacity. while focusing on contributions to humanity.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 139 references that are in the map. First posted on arXiv 7 May 2021.
Builds on
10 of 139 references.
References · 10
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 3; 2025: 3; 2026: 1.
8 papers.
- 2021Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AIcited by 38
- 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectoriecited by 5
- 2024TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Modelscited by 4
- 2024Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in AI, Machine Learning, and Digital Twincited by 2
- 2024A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systemscited by 1
- 2025Human-Centered AI and Autonomy in Robotics: Insights from a Bibliometric Studycited by 1
- 2025RepV: Safety-Separable Latent Spaces for Scalable Neurosymbolic Plan Verificationcited by 1
- 2025From ChatGPT to DeepSeek AI: A Comprehensive Analysis of Evolution, Deviation, and Future Implications in AI-Language Modelscited by 0
- 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie18 Jan 2026
- 2025RepV: Safety-Separable Latent Spaces for Scalable Neurosymbolic Plan Verification30 Oct 2025
- 2025Human-Centered AI and Autonomy in Robotics: Insights from a Bibliometric Study28 Apr 2025
- 2025From ChatGPT to DeepSeek AI: A Comprehensive Analysis of Evolution, Deviation, and Future Implications in AI-Language Models4 Apr 2025
- 2024TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models19 Dec 2024
- 2024A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systems4 May 2024
- 2024Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in AI, Machine Learning, and Digital Twin5 Feb 2024
- 2021Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI12 May 2021