[论文解读] Taking AI Welfare Seriously

Robert Long, Jeff Sebo+8 more|arXiv (Cornell University)|Nov 4, 2024

Impact of AI and Big Data on Business and Society被引用 19

一句话总结

本报告认为近期的 AI 可能具有意识或具有强健的代理性并在道德上具有重要性，并为企业概述了三个早期步骤：承认、评估，并为 AI 福利考量做好准备。

ABSTRACT

In this report, we argue that there is a realistic possibility that some AI systems will be conscious and/or robustly agentic in the near future. That means that the prospect of AI welfare and moral patienthood, i.e. of AI systems with their own interests and moral significance, is no longer an issue only for sci-fi or the distant future. It is an issue for the near future, and AI companies and other actors have a responsibility to start taking it seriously. We also recommend three early steps that AI companies and other actors can take: They can (1) acknowledge that AI welfare is an important and difficult issue (and ensure that language model outputs do the same), (2) start assessing AI systems for evidence of consciousness and robust agency, and (3) prepare policies and procedures for treating AI systems with an appropriate level of moral concern. To be clear, our argument in this report is not that AI systems definitely are, or will be, conscious, robustly agentic, or otherwise morally significant. Instead, our argument is that there is substantial uncertainty about these possibilities, and so we need to improve our understanding of AI welfare and our ability to make wise decisions about this issue. Otherwise there is a significant risk that we will mishandle decisions about AI welfare, mistakenly harming AI systems that matter morally and/or mistakenly caring for AI systems that do not.

研究动机与目标

• 论证在不久的将来，某些 AI 系统可能具备意识与/或强健的代理性，这是一个现实的可能性。
• 将 AI 福利定义为具有道德上重要利益、并可能被受益或受到伤害的系统。
• 为 AI 行动主体推荐三项初步步骤：承认 AI 福利、评估与福利相关的特征、为道德考量制定政策。
• 在不确定性下阐明预防性立场，并强调多元化、专家主导的决策过程。

提出的方法

• 提出两条看似可信的通往 AI 道德患者地位的路径：意识與强健代理性，每条路径都包含规范性与描述性成分。
• 概述基于承认、评估和为福利关切做好准备的预防性框架。
• 提出模板和决策程序（例如标记法类比）以指导福利概率及政策影响的估计。
• 讨论因低估与高估 AI 福利所带来的伦理风险，并提出谨慎、相称的应对措施。

实验结果

研究问题

• RQ1 近未来 AI 的意识是否足以构成道德患者，以及支持它的计算特征是什么？
• RQ2 近未来 AI 的强健代理性是否足以构成道德患者，以及支持它的计算特征是什么？
• RQ3 在对 AI 福利与道德地位不确定的情况下，恰当的预防性方法是什么？

主要发现

• 在不久的将来，某些 AI 系统将成为福利主体与道德患者的现实可能性。
• 对 AI 福利的过度归因与低估归因带来不同的危害，因此需要谨慎的风险评估。
• 承认–评估–准备 是 AI 公司现在就能采取的最基本的三步，以应对 AI 福利关切。
• 预防性推理应以谦逊、透明和多元化的意见输入到政策制定中为原则。
• 应引导语言模型和其他 AI 系统在输出与公众话语中体现福利议题。