Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates
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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
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
- 2020Artificial Intelligence, Values and Alignmentcited by 448
- 2016Cooperative Inverse Reinforcement Learningcited by 300
- 2017Data Augmentation Generative Adversarial Networkscited by 278
- 2021Core Challenges of Social Robot Navigation: A Surveycited by 106
- 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithmscited by 56
7 more
- 2021Conflict Avoidance in Social Navigation -- a Surveycited by 14
- 2013The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Userscited by 11
- 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
- 2023Exploring the Cost of Interruptions in Human-Robot Teamingcited by 5
- 2024ODGR: Online Dynamic Goal Recognitioncited by 5
- 2025Birds of a Different Feather Flock Together: Exploring Opportunities and Challenges in Animal-Human-Machine Teamingcited by 1
- 2025General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learningcited by 1
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026GRAIL: Goal Recognition Alignment through Imitation Learningcited by 3
- 2026The Intelligent Disobedience Game: Formulating Disobedience in Stackelberg Games and Markov Decision Processescited by 0
- 2026Beyond Compliance: How AI Could Help Creative Writers by Refusing Themcited by 0
- 2026Towards Responsibly Non-Compliant Machinescited by 0
- 2026Towards Responsibly Non-Compliant Machines10 Jun 2026
- 2026Beyond Compliance: How AI Could Help Creative Writers by Refusing Them3 Apr 2026
- 2026The Intelligent Disobedience Game: Formulating Disobedience in Stackelberg Games and Markov Decision Processes22 Mar 2026
- 2026GRAIL: Goal Recognition Alignment through Imitation Learning15 Feb 2026