Related papers: AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems

AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems

URL: http://arxiv.org/abs/2607.00523v1
Date: Wed, 01 Jul 2026 07:10:16 GMT
Title: AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems
Abstract summary: I argue that within highly impactful areas there are grounds for treating autonomous and opaque systems as analogous to dogs.<n>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"<n>This recasting of roles shifts the way we view humans, AI-enabled and autonomous systems, and the relations between them.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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.

Related papers

Reversing the Paradigm: Building AI-First Systems with Human Guidance [0.0]
The relationship between humans and artificial intelligence is no longer science fiction.<n>Rather than replacing humans, AI augments tasks, enhancing decisions with data.<n>The future of work is toward AI agents handling tasks autonomously.<n>This paper examines the technological and organizational changes needed to enable responsible adoption of AI-first systems.
arXiv Detail & Related papers (2025-06-13T21:48:44Z)
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy [36.74150011862134]
We show how human-AI teamwork can handle complex tasks better than AI working alone.<n>This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans.
arXiv Detail & Related papers (2025-06-11T06:08:13Z)
Agency in Artificial Intelligence Systems [0.0]
There is a general concern that present developments in artificial intelligence (AI) research will lead to sentient AI systems.<n>But why cannot sentient AI systems benefit humanity instead?<n>I ask whether a putative AI system will develop an altruistic or a malicious disposition towards our society, or what would be the nature of its agency.
arXiv Detail & Related papers (2025-02-09T02:21:14Z)
Aligning Generalisation Between Humans and Machines [74.120848518198]
AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
arXiv Detail & Related papers (2024-11-23T18:36:07Z)
Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We examine what is known about human wisdom and sketch a vision of its AI counterpart.<n>We argue that AI systems particularly struggle with metacognition.<n>We discuss how wise AI might be benchmarked, trained, and implemented.
arXiv Detail & Related papers (2024-11-04T18:10:10Z)
Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
arXiv Detail & Related papers (2024-07-03T15:38:57Z)
Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
arXiv Detail & Related papers (2024-04-29T04:51:05Z)
Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components. We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z)
Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety [2.3572498744567127]
We argue that alignment to human intent is insufficient for safe AI systems. We argue that preservation of long-term agency of humans may be a more robust standard.
arXiv Detail & Related papers (2023-05-30T17:14:01Z)
Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z)
Meaningful human control over AI systems: beyond talking the talk [8.351027101823705]
We identify four properties which AI-based systems must have to be under meaningful human control. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human's ability and authority to control the system.
arXiv Detail & Related papers (2021-11-25T11:05:37Z)
Learning to Complement Humans [67.38348247794949]
A rising vision for AI in the open world centers on the development of systems that can complement humans for perceptual, diagnostic, and reasoning tasks. We demonstrate how an end-to-end learning strategy can be harnessed to optimize the combined performance of human-machine teams.
arXiv Detail & Related papers (2020-05-01T20:00:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.