# The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems

arXiv: https://arxiv.org/abs/2105.04408 · HTML: https://mlsanity.com/paper/2105.04408/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 139 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [A Survey of Predictive Maintenance: Systems, Purposes and Approaches](https://mlsanity.com/paper/1912.07383/) (2019)
- [Normalization in Training U-Net for 2D Biomedical Semantic Segmentation](https://mlsanity.com/paper/1809.03783/) (2018)
- [Probabilistic Model Checking of Robots Deployed in Extreme Environments](https://mlsanity.com/paper/1812.04128/) (2018)
- [A Survey on Deep Learning Methods for Robot Vision](https://mlsanity.com/paper/1803.10862/) (2018)
- [Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy](https://mlsanity.com/paper/1810.01835/) (2018)
- [When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions](https://mlsanity.com/paper/2101.10819/) (2021)
- [Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement?](https://mlsanity.com/paper/1907.12092/) (2019)

## Built on by (most cited first, 8 of 8)

- [Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI](https://mlsanity.com/paper/2105.05424/) (2021)
- [Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie](https://mlsanity.com/paper/2601.12554/) (2026)
- [TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models](https://mlsanity.com/paper/2412.15462/) (2024)
- [Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in AI, Machine Learning, and Digital Twin](https://mlsanity.com/paper/2402.02980/) (2024)
- [RepV: Safety-Separable Latent Spaces for Scalable Neurosymbolic Plan Verification](https://mlsanity.com/paper/2510.26935/) (2025)
- [A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systems](https://mlsanity.com/paper/2405.02726/) (2024)
- [Human-Centered AI and Autonomy in Robotics: Insights from a Bibliometric Study](https://mlsanity.com/paper/2504.19848/) (2025)
- [From ChatGPT to DeepSeek AI: A Comprehensive Analysis of Evolution, Deviation, and Future Implications in AI-Language Models](https://mlsanity.com/paper/2504.03219/) (2025)

## 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.