[论文解读] Agentic AI: Autonomy, Accountability, and the Algorithmic Society

Anirban Mukherjee, Hannah Hanwen Chang|ArXiv.org|Feb 1, 2025

Reinforcement Learning in Robotics被引用 7

一句话总结

本文分析自主追求长期目标的具代理性的 AI，考察创造力、法律/伦理考量，以及竞争效果，并主张在自治与问责之间取得平衡的框架。

ABSTRACT

Agentic Artificial Intelligence (AI) can autonomously pursue long-term goals, make decisions, and execute complex, multi-turn workflows. Unlike traditional generative AI, which responds reactively to prompts, agentic AI proactively orchestrates processes, such as autonomously managing complex tasks or making real-time decisions. This transition from advisory roles to proactive execution challenges established legal, economic, and creative frameworks. In this paper, we explore challenges in three interrelated domains: creativity and intellectual property, legal and ethical considerations, and competitive effects. Central to our analysis is the tension between novelty and usefulness in AI-generated creative outputs, as well as the intellectual property and authorship challenges arising from AI autonomy. We highlight gaps in responsibility attribution and liability that create a "moral crumple zone"--a condition where accountability is diffused across multiple actors, leaving end-users and developers in precarious legal and ethical positions. We examine the competitive dynamics of two-sided algorithmic markets, where both sellers and buyers deploy AI agents, potentially mitigating or amplifying tacit collusion risks. We explore the potential for emergent self-regulation within networks of agentic AI--the development of an "algorithmic society"--raising critical questions: To what extent would these norms align with societal values? What unintended consequences might arise? How can transparency and accountability be ensured? Addressing these challenges will necessitate interdisciplinary collaboration to redefine legal accountability, align AI-driven choices with stakeholder values, and maintain ethical safeguards. We advocate for frameworks that balance autonomy with accountability, ensuring all parties can harness agentic AI's potential while preserving trust, fairness, & societal welfare.

研究动机与目标

• 评估具代理性的 AI 在创造力和知识产权方面的挑战。
• 评估因自主 AI 行为产生的法律、伦理和问责缺口。
• 分析在具代理性 AI 代理人参与的双边算法市场中的竞争动态。
• 考虑在具代理性 AI 网络中可能出现的自治自我调控以及“算法社会”的潜在可能。
• 提出在自治与问责之间取得平衡并维护社会福祉的框架。

提出的方法

• 对具代理性的 AI 与传统生成式 AI 的概念分析。
• 审查 AI 生成输出中的新颖性与有用性及相关的知识产权/署名问题。
• 评估责任归属与诉讼责任缺口，包括“道德挫折区”（moral crumple zone）。
• 分析双边算法市场以理解潜在串谋与竞争效应。
• 讨论在具代理性的 AI 网络中涌现的自治自我调控与社会规范的发展。

实验结果

研究问题

• RQ1 自主 AI 行为对创造力的知识产权、署名与所有权含义是什么？
• RQ2 在具代理性 AI 情景中，责任与赔偿如何在用户、开发者和代理之间分配？
• RQ3 在具自主代理的双边 AI 市场中会出现哪些竞争动态？
• RQ4 是否可能发展出与社会价值观相一致的涌现式算法社会，以及如何确保透明度？

主要发现

• 具代理性的 AI 引入自主性，挑战现有法律与伦理框架。
• 责任归属的缺口可能使问责扩散到多个参与方，造成“道德挫折区”。
• 具有 AI 代理的双边算法市场可能改变买方与卖方之间的潜在默契/共谋动态。
• AI 网络中的涌现式自我调控可能形成一个“算法社会”，并带来潜在的非预期后果。
• 需要跨学科框架以将法律问责重新对齐到利益相关方的价值观和伦理保障。