aiqbench analysis
This paper proposes a framework for engineering autonomous agents that can responsibly refuse harmful requests, addressing the nuanced mechanics of task refusal and override pathways. By anchoring non-compliance in justifications and liability tracking, the work shifts focus from simple obedience to managing security risks and ethical boundaries in AI systems. This research is significant for developers building safety layers into next-generation agents, particularly as they navigate the tension between user autonomy and system safety.
Towards Responsibly Non-Compliant Machines
arXiv:2606.12147v1 Announce Type: new Abstract: We consider the problem of engineering autonomous intelligent agents that are capable to responsibly not comply with user requests. We argue that machine non-compliance comes in many different forms, and sketch the issues we should pursue on the road of accomplishing responsibly non-compliant intelligent machines. We anchor responsible non-compliance in justifications for task refusal, pathways to override the non-compliance, as well as careful tracking of security risks and liability transfers.
Abstract:We consider the problem of engineering autonomous intelligent agents that are capable to responsibly not comply with user requests. We argue that machine non-compliance comes in many different forms, and sketch the issues we should pursue on the road of accomplishing responsibly non-compliant intelligent machines. We anchor responsible non-compliance in justifications for task refusal, pathways to override the non-compliance, as well as careful tracking of security risks and liability transfers.
Artificial Intelligence (cs.AI)
arXiv:2606.12147 [cs.AI]
(or arXiv:2606.12147v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.12147 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 164 words
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