AI Agents Push Humans Out of the Loop

• URL: http://arxiv.org/abs/2608.23642v1
• Date: Mon, 24 Aug 2026 02:58:02 GMT
• Title: AI Agents Push Humans Out of the Loop
• Abstract summary: We argue that current approaches to the development and deployment of AI agent systems do not support effective human oversight.<n>To address this, a top priority in the advancement of AI agents should be supporting the situated goals and cognitive requirements of effective human oversight.
• Score: 6.787455541983529
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI agents pose significant risks as they are granted increasing autonomy. A commonly proposed solution is human oversight and keeping a ''human in the loop'', but this is not a simple solution: Not only do current approaches to AI agent design impede effective human oversight, but the cognitive capacities required for it are also themselves degraded by extended use of AI systems. This position paper argues that current approaches to the development and deployment of AI agent systems do not support effective human oversight -- they contribute to its degradation. To address this, a top priority in the advancement of AI agents should be supporting the situated goals and cognitive requirements of effective human oversight, treating the human needs of overseers at the same level of importance as AI agent capability. To put this idea into practice, we connect work on automation and human-computer interaction to AI agent processes, outlining design-level affordances and organizational protocols that (1) support overseers in exercising critical judgement and (2) counteract the skill atrophy that arises from extended use of automation. We urge developers and deployers to adopt these or similar approaches. Without explicit support for the cognitive demands of effective human-agent interaction, AI agent systems will continue to passively incentivize the degradation of the very human skills they rely on.

Related papers

• ComBodied Agents: a New Paradigm of Human-Centric Agentic AI [121.60938707072069]
  We introduce Combodied Agents, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time.<n>We unify fragmented capabilities across personal assistants, health agents, AI companions, and adaptive human--AI systems into a closed loop.
  arXiv  Detail & Related papers  (2026-08-11T13:35:58Z)
• The Boundaries of Automation: A Theory of Persistent Human Participation [107.84677954791773]
  We argue that human participation may persist even with highly capable AI systems.<n>In some activities, the target is not fully specified in advance but instead emerges through the interaction itself.<n>This perspective has important implications for the limits of automation.
  arXiv  Detail & Related papers  (2026-07-23T17:33:43Z)
• AI and Human Oversight: A Risk-Based Framework for Alignment [0.2039123720459736]
  This paper examines strategies for designing AI systems that uphold fundamental rights, strengthen human agency, and embed effective human oversight mechanisms.<n>By linking the level of AI model risk to the appropriate form of human oversight, the paper underscores the critical role of human involvement in the responsible deployment of AI.
  arXiv  Detail & Related papers  (2025-10-10T07:36:44Z)
• HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants [5.4831302830611195]
  We develop the idea of human agency by integrating philosophical and scientific theories of agency with AI-assisted evaluation methods.<n>We develop HumanBench (HAB), a scalable and adaptive benchmark with six dimensions of human agency based on typical AI use cases.
  arXiv  Detail & Related papers  (2025-09-10T11:10:10Z)
• Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism [48.41735416075536]
  Interactive Imitation Learning (IIL) allows agents to acquire desired behaviors through human interventions.<n>We propose the Adaptive Intervention Mechanism (AIM), a novel robot-gated IIL algorithm that learns an adaptive criterion for requesting human demonstrations.
  arXiv  Detail & Related papers  (2025-06-10T18:43:26Z)
• Envisioning an AI-Enhanced Mental Health Ecosystem [1.534667887016089]
  We explore various AI applications in peer support, self-help interventions, proactive monitoring, and data-driven insights.<n>We propose a hybrid ecosystem where AI assists but does not replace human providers, emphasising responsible deployment and evaluation.
  arXiv  Detail & Related papers  (2025-03-19T04:21:38Z)
• Fully Autonomous AI Agents Should Not be Developed [50.61667544399082]
  This paper argues that fully autonomous AI agents should not be developed.<n>In support of this position, we build from prior scientific literature and current product marketing to delineate different AI agent levels.<n>Our analysis reveals that risks to people increase with the autonomy of a system.
  arXiv  Detail & Related papers  (2025-02-04T19:00:06Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• 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)
• A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents [0.0]
  We investigate the use of cognitively inspired models of behavior to predict the behavior of both human and AI agents. The predicted behavior is used to delegate control between humans and AI agents through the use of an intermediary entity.
  arXiv  Detail & Related papers  (2022-04-06T15:15:21Z)
• 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)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.