[论文解读] The Human-AI Handshake Framework: A Bidirectional Approach to Human-AI Collaboration

Aung Pyae|ArXiv.org|Feb 3, 2025

Human-Automation Interaction and Safety被引用 3

一句话总结

本论文提出一种名为“ Human-AI Handshake Model”的双向、适应性人机协作框架，概述了五个关键属性并使 AI 能作为一个响应式伙伴。

ABSTRACT

Human-AI collaboration is evolving from a tool-based perspective to a partnership model where AI systems complement and enhance human capabilities. Traditional approaches often limit AI to a supportive role, missing the potential for reciprocal relationships where both human and AI inputs contribute to shared goals. Although Human-Centered AI (HcAI) frameworks emphasize transparency, ethics, and user experience, they often lack mechanisms for genuine, dynamic collaboration. The "Human-AI Handshake Model" addresses this gap by introducing a bi-directional, adaptive framework with five key attributes: information exchange, mutual learning, validation, feedback, and mutual capability augmentation. These attributes foster balanced interaction, enabling AI to act as a responsive partner, evolving with users over time. Human enablers like user experience and trust, alongside AI enablers such as explainability and responsibility, facilitate this collaboration, while shared values of ethics and co-evolution ensure sustainable growth. Distinct from existing frameworks, this model is reflected in tools like GitHub Copilot and ChatGPT, which support bi-directional learning and transparency. Challenges remain, including maintaining ethical standards and ensuring effective user oversight. Future research will explore these challenges, aiming to create a truly collaborative human-AI partnership that leverages the strengths of both to achieve outcomes beyond what either could accomplish alone.

研究动机与目标

• 将人机协作从基于工具的模式转向基于伙伴关系的模式的动机
• 定义一个双向框架，使人类与 AI 之间能够互相输入
• 识别核心使能因素（人为因素与 AI 因素）以及可持续协作的共同伦理
• 提出一组可操作的属性，以指导动态的人机交互

提出的方法

• 引入框架的五个关键属性：信息交流、互相学习、验证、反馈和共同能力增强
• 描述这些属性如何使 AI 作为一个能够随着用户一起演化的响应式伙伴来运作
• 讨论人为使能因素（经验、信任）和 AI 使能因素（可解释性、责任性）在塑造协作中的作用
• 强调共享价值观（如伦理和共进化）对可持续增长的重要性

实验结果

研究问题

• RQ1 哪些机制实现人机之间的互惠信息交流与学习
• RQ2 如何构建验证与反馈以维持人机合作中的信任与一致性
• RQ3 双方在长期内维持双向协作的促成因素有哪些
• RQ4 像 GitHub Copilot 和 ChatGPT 等现有工具如何体现双向学习与透明度

主要发现

• 该框架指出了五个有助于人机双向互动的属性
• 人为使能因素（经验与信任）与 AI 使能因素（可解释性与责任）对协作至关重要
• 伦理与共进化被提出作为指导可持续人机伙伴关系的共同价值观
• 该模型被定位为与传统工具的区别在于强调互惠学习与响应性
• 挑战包括在实际部署中维持伦理标准并确保对用户的有效监督