提出兼顾公平与隐私的机器人决策框架，解决生成式AI的伦理风险。
Fairness risk and its privacy-enabled solution in AI-driven robotic applications
- 构建用户效用感知的公平性度量，融合数据随机性与隐私预算。
- 实验证明隐私保护可反向促进公平目标达成，二者可协同优化。
- 适合关注机器人伦理、隐私与公平的AI研究者与工程师。
自主机器与算法的复杂决策将塑造未来社会，生成式AI正成为关键推动力。然而我们发现，生成式AI驱动的发展存在严重公平性隐患。在机器人应用中，尽管公平性直觉普遍存在，但缺乏能捕捉用户效用与数据内在随机性的精确、可实施定义。本文提出一种面向用户效用的公平性度量，并联合分析公平性与用户数据隐私，推导出隐私预算如何制约公平性指标的条件。由此建立统一框架，形式化并量化公平性及其与隐私的交互关系，并在机器人导航任务中验证。考虑到多数机器人系统依法需保障用户隐私，本方法显示：此类隐私预算可被协同用于实现公平性目标。在隐私与公平的协同考量下解决公平性问题，是迈向人工智能伦理化应用的重要一步，有助于增强公众对日常环境中部署的自主机器人之信任。
原文摘要 · Abstract (English)
Complex decision-making by autonomous machines and algorithms could underpin the foundations of future society. Generative AI is emerging as a powerful engine for such transitions. However, we show that Generative AI-driven developments pose a critical pitfall: fairness concerns. In robotic applications, although intuitions about fairness are common, a precise and implementable definition that captures user utility and inherent data randomness is missing. Here we provide a utility-aware fairness metric for robotic decision making and analyze fairness jointly with user-data privacy, deriving conditions under which privacy budgets govern fairness metrics. This yields a unified framework that formalizes and quantifies fairness and its interplay with privacy, which is tested in a robot navigation task. In view of the fact that under legal requirements, most robotic systems will enforce user privacy, the approach shows surprisingly that such privacy budgets can be jointly used to meet fairness targets. Addressing fairness concerns in the creative combined consideration of privacy is a step towards ethical use of AI and strengthens trust in autonomous robots deployed in everyday environments.
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