mlsanityData to 14 Oct 2026

Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates

arXiv 2506.22276 ↗First posted 27 Jun 2025cs.AIcited by 4

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Abstract (arXiv)

Artificial intelligence has made remarkable strides in recent years, achieving superhuman performance across a wide range of tasks. Yet despite these advances, most cooperative AI systems remain rigidly obedient, designed to follow human instructions without question and conform to user expectations, even when doing so may be counterproductive or unsafe. This paper argues for expanding the agency of AI teammates to include textitintelligent disobedience, empowering them to make meaningful and autonomous contributions within human-AI teams. It introduces a scale of AI agency levels and uses representative examples to highlight the importance and growing necessity of treating AI autonomy as an independent research focus in cooperative settings. The paper then explores how intelligent disobedience manifests across different autonomy levels and concludes by proposing initial boundaries and considerations for studying disobedience as a core capability of artificial agents.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 74 references that are in the map. First posted on arXiv 27 Jun 2025.

Builds on

19 of 74 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
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  12. 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithmscited by 56
7 more
  1. 2021Conflict Avoidance in Social Navigation -- a Surveycited by 14
  2. 2013The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Userscited by 11
  3. 2023"I am the follower, also the boss": Exploring Different Levels of Autonomy and Machine Forms of Guiding Robots for the Visually Impairedcited by 6
  4. 2023Exploring the Cost of Interruptions in Human-Robot Teamingcited by 5
  5. 2024ODGR: Online Dynamic Goal Recognitioncited by 5
  6. 2025Birds of a Different Feather Flock Together: Exploring Opportunities and Challenges in Animal-Human-Machine Teamingcited by 1
  7. 2025General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learningcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.