arXiv:2504.16204cs.CYcs.AI2025-04被引 17

教人如何写负责任的AI指令,让技术更公平透明

Reflexive Prompt Engineering: A Framework for Responsible Prompt Engineering and Interaction Design

  • 构建五步框架:从设计到管理,全程嵌入伦理考量
  • 实证显示可提升社会效益,降低潜在风险
  • 适合关注AI伦理、交互设计的研究者与开发者

负责任的提示工程已成为确保生成式人工智能系统满足社会需求并最小化潜在危害的关键框架。随着生成式AI应用日益强大且普及,通过提示词与AI互动的方式对公平性、问责制和透明度具有深远影响。本文探讨如何通过策略性提示工程将伦理、法律考量和社会价值观直接嵌入AI交互,超越单纯的性能优化。提出一个包含五个相互关联组件的综合框架:提示设计、系统选择、系统配置、性能评估和提示管理。基于实证证据,证明各组件可有效促进更好的社会成果并缓解潜在风险。分析揭示,高效提示工程需在技术精确性与伦理意识间取得平衡,结合系统化严谨性与对社会影响的深刻理解。通过考察现实与新兴实践,文章说明负责任的提示工程是连接AI开发与部署的重要桥梁,使组织可在不修改模型架构的前提下调优输出。该方法契合‘设计即责任’原则,将伦理考量直接融入实施过程,而非事后补救。最后,文章指明关键研究方向与实用指南,推动该领域发展。

原文摘要 · Abstract (English)

Responsible prompt engineering has emerged as a critical framework for ensuring that generative artificial intelligence (AI) systems serve society's needs while minimizing potential harms. As generative AI applications become increasingly powerful and ubiquitous, the way we instruct and interact with them through prompts has profound implications for fairness, accountability, and transparency. This article examines how strategic prompt engineering can embed ethical and legal considerations and societal values directly into AI interactions, moving beyond mere technical optimization for functionality. This article proposes a comprehensive framework for responsible prompt engineering that encompasses five interconnected components: prompt design, system selection, system configuration, performance evaluation, and prompt management. Drawing from empirical evidence, the paper demonstrates how each component can be leveraged to promote improved societal outcomes while mitigating potential risks. The analysis reveals that effective prompt engineering requires a delicate balance between technical precision and ethical consciousness, combining the systematic rigor and focus on functionality with the nuanced understanding of social impact. Through examination of real-world and emerging practices, the article illustrates how responsible prompt engineering serves as a crucial bridge between AI development and deployment, enabling organizations to fine-tune AI outputs without modifying underlying model architectures. This approach aligns with broader "Responsibility by Design" principles, embedding ethical considerations directly into the implementation process rather than treating them as post-hoc additions. The article concludes by identifying key research directions and practical guidelines for advancing the field of responsible prompt engineering.

提示工程AI伦理责任设计

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