当AI完成工作时，学习的目的是什么？后工具性学习与能力消解的风险
When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution
浏览论文内容
中文总结 AI 辅助
本文针对AI完成机构判定能力的工作时学习目的模糊的问题，提出后工具性学习概念，分析其保留的五种能力及能力消解风险，核心指出AI治理需评估系统部署是否保留人的相关能力。
中文摘要 AI 辅助
随着AI系统能够产出机构通常用以判定能力的论文、代码、报告、摘要、计划和决策，一个熟悉的问题愈发难以回答：学习的目的是什么？现有的AI伦理研究恰当地强调了当前存在的缺陷——偏见、不透明、幻觉、劳动剥削、隐私风险以及问责性薄弱。但如果学习的理由仅基于这些缺陷，那么每一次技术改进似乎都在削弱这一理由。本文提出了一个不同的答案：通过将AI理想化为能完美执行指定任务但不具备目的、合法性和责任权威的系统，我们提出后工具性学习的概念——这种学习能在许多有用产出可被委托时，保留个人和机构所需的五种能力：目标设定、理由阐述、可争辩性、拒绝/修正以及参与，并将这些能力的侵蚀命名为能力消解。核心案例是生成式AI下的评估：当精美的产出不再可靠地证明理解时，机构必须评估学习者与AI介导工作的问责关系，而非仅评估产出本身。实际结论是：AI治理不仅应评估系统是否表现良好，还应评估其部署是否让人们能够理解、挑战、修正并分担这些系统所介导实践的责任。
英文摘要
As AI systems become capable of producing the essays, code, reports, summaries, plans, and decisions through which institutions usually recognize competence, a familiar question becomes harder to answer: what is learning for? Existing AI ethics rightly emphasizes present failures--bias, opacity, hallucination, labor extraction, privacy risk, and weak accountability. But if the case for learning rests only on those failures, then each technical improvement appears to weaken it. This article develops a different answer. Using the idealization of AI that executes specified tasks flawlessly while lacking authority over purposes, legitimacy, and responsibility, we argue for post-instrumental learning: learning that preserves the capacities people and institutions need when many useful outputs can be delegated. We analyze five such capacities--end-setting, reason-giving, contestability, refusal/revision, and participation--and name their erosion capacity dissolution. The central case is assessment under generative AI. When a polished artifact no longer reliably evidences understanding, institutions must assess the learner's accountable relation to AI-mediated work rather than the artifact alone. The takeaway is practical: AI governance should evaluate not only whether systems perform well, but also whether their deployment leaves people able to understand, challenge, revise, and share responsibility for the practices those systems mediate.
