We Need a New Ethics for a World of AI Agents
- URL: http://arxiv.org/abs/2509.10289v1
- Date: Fri, 12 Sep 2025 14:29:14 GMT
- Title: We Need a New Ethics for a World of AI Agents
- Authors: Iason Gabriel, Geoff Keeling, Arianna Manzini, James Evans,
- Abstract summary: We argue for greater engagement by scientists, scholars, engineers and policymakers.<n>We explore key challenges that must be addressed to ensure that interactions between humans and agents, and among agents themselves, remain broadly beneficial.
- Score: 3.787422067741547
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The deployment of capable AI agents raises fresh questions about safety, human-machine relationships and social coordination. We argue for greater engagement by scientists, scholars, engineers and policymakers with the implications of a world increasingly populated by AI agents. We explore key challenges that must be addressed to ensure that interactions between humans and agents, and among agents themselves, remain broadly beneficial.
Related papers
- AI & Human Co-Improvement for Safer Co-Superintelligence [34.07423327792328]
We advocate that a more achievable and better goal for humanity is to maximize co-improvement: collaboration between human researchers and AIs to achieve co-superintelligence.<n>That is, specifically targeting improving AI systems' ability to work with human researchers to conduct AI research together.
arXiv Detail & Related papers (2025-12-05T01:50:23Z) - The Singapore Consensus on Global AI Safety Research Priorities [128.58674892183657]
"2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety" aimed to support research in this space.<n>Report builds on the International AI Safety Report chaired by Yoshua Bengio and backed by 33 governments.<n>Report organises AI safety research domains into three types: challenges with creating trustworthy AI systems (Development), challenges with evaluating their risks (Assessment) and challenges with monitoring and intervening after deployment (Control)
arXiv Detail & Related papers (2025-06-25T17:59:50Z) - 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) - Agentic AI and the Cyber Arms Race [3.0198881680567635]
Agentic AI is shifting the cybersecurity landscape as attackers and defenders leverage AI agents to augment humans and automate common tasks.<n>In this article, we examine the implications for cyber warfare and global politics as Agentic AI becomes more powerful and enables the broad proliferation of capabilities only available to the most well resourced actors today.
arXiv Detail & Related papers (2025-02-10T16:06:29Z) - 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) - Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
AI systems need to be explainable to the humans in the loop.
We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication.
While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
arXiv Detail & Related papers (2024-05-19T22:22:21Z) - The Ethics of Advanced AI Assistants [53.89899371095332]
This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants.
We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user.
We consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants.
arXiv Detail & Related papers (2024-04-24T23:18:46Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - The Rise of the AI Co-Pilot: Lessons for Design from Aviation and Beyond [22.33734581699234]
We advocate for a paradigm where AI is seen as a collaborative co-pilot, working under human guidance rather than as a mere tool.
Our paper proposes a design approach that emphasizes active human engagement, control, and skill enhancement in the AI partnership.
arXiv Detail & Related papers (2023-11-16T13:58:15Z) - 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) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - Watch-And-Help: A Challenge for Social Perception and Human-AI
Collaboration [116.28433607265573]
We introduce Watch-And-Help (WAH), a challenge for testing social intelligence in AI agents.
In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently.
We build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines.
arXiv Detail & Related papers (2020-10-19T21:48:31Z)
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.