arXiv:2606.07211v1 Announce Type: cross Abstract: Autonomous robotic systems are being proposed for use in hazardous environments, often to reduce the risks to human workers. In the immediate future, it is likely that human workers will continue to use and direct these autonomous robots, much like other computerised tools but with more sophisticated decision-making. Therefore, one important area on which to focus engineering effort is ensuring that these users trust the system. Recent literature suggests that explainability is closely related to how trustworthy a system is. Like safety and sec
As autonomous systems become more sophisticated and deployed in complex environments, the need for human trust and explainability is becoming a critical bottleneck for wide-scale adoption.
This research directly addresses a key barrier to the deployment of advanced AI in critical sectors like defence and hazardous operations, where trust and understanding are paramount for human-robot collaboration.
The focus on abstract architectures for explainable autonomy suggests a move towards embedding explainability as a core design principle rather than an afterthought, enhancing eventual system trustworthiness and regulatory acceptance.
- · Defence contractors
- · Hazardous environment industries
- · AI ethics and safety researchers
- · Robotics companies
- · Companies with black-box AI systems
- · Traditional manual labour in hazardous environments
Increased development and adoption of explainable AI in robotics for critical applications.
Faster integration of autonomous systems into sensitive human-supervised roles due to enhanced trust and oversight capabilities.
New regulatory frameworks and certification standards for autonomous systems based on their explainability, potentially enabling broader deployment without excessive human safety overhead.
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Read at arXiv cs.AI