AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems
2026-07-01 • Human-Computer Interaction
Human-Computer InteractionArtificial IntelligenceComputers and SocietyEmerging Technologies
AI summaryⓘ
The authors suggest that when humans work with advanced AI systems, especially in sensitive fields like medicine or the military, we should think of these AI systems like trained animals, such as dogs. Instead of just using the AI, humans would act as 'handlers' who guide and take responsibility for the AI's actions. This idea helps clarify who is responsible when the AI does something important or unexpected. While the comparison to animals isn't perfect, the authors use it as a helpful starting point to better understand human-AI teamwork. Ultimately, they believe humans and AI should be seen as partners working together on complex tasks, not just tools being used.
Artificial IntelligenceAutonomous SystemsHuman-Machine TeamingEthicsResponsibilityAI TransparencyHuman-AI InteractionMachine-Animal AnalogyAccountability
Authors
Nathan G. Wood
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 of these systems are not to be viewed as "users" or "deployers" of these systems, but instead take the role of "handlers". This recasting of roles shifts the way we view humans, AI-enabled and autonomous systems, and the relations between them, and moreover clarifies the clear and traceable lines of responsibility humans have for the outcomes brought about when using these systems. In developing this point, I clarify that the machine-animal analogy does admit disanalogous elements, but that its touch-points ground it as a starting point. I then explore how we can divest the humans-as-handlers approach of those aspects of our relationships with animals which are unfitting for how we engage with and make use of autonomous and AI-enabled systems. I conclude by arguing that the trajectory of human-machine teamings for autonomous and AI-enabled systems should be a state where we authentically view these not as artifacts which we simply make use of, but as collaborators with which we pursue complex goals and carry out complex tasks.