AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems
arXiv:2607.00523v1 Announce Type: cross Abstract: Artificial intelligence (AI) is becoming ubiquitous, and across domains, increasingly autonomous systems are carrying out tasks which raise significant ethical and legal challenges which demonstrate a need for strong human-machine teams rooted in trust. In this article, I argue that within highly impactful areas (such as medicine or warfighting) there are grounds for us initially treating autonomous and opaque systems as relevantly analogous to dogs (or other animals with which we have close relationships). Under this analogy, humans making use
As AI systems become more autonomous and integrated into high-stakes domains, the need to define robust human-AI interaction models grounded in trust is immediate.
This paper proposes a novel framework for human-AI teaming that directly addresses ethical, legal, and trust challenges, crucial for the strategic deployment and adoption of advanced AI.
The analogy of humans-as-handlers shifts the perception of autonomous AI from a pure algorithmic tool to a responsible entity requiring a relationship akin to managing a working animal.
- · AI developers focused on explainability
- · High-stakes industries (e.g., medicine, defense)
- · Regulatory bodies developing AI governance frameworks
- · AI developers prioritizing full autonomy over human oversight
- · Entities ignoring ethical AI deployment
- · Systems lacking transparency and auditability
Closer scrutiny and regulatory frameworks for autonomous AI in critical applications will emerge.
Increased investment in human-machine interface design and AI explainability will become standard.
The legal and ethical liability frameworks for AI will evolve to incorporate concepts of 'handling' and 'supervision'.
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Read at arXiv cs.AI