Fairness risk and its privacy-enabled solution in AI-driven robotic applications

2026-01-13

AI Summary

This work addresses the absence of actionable fairness definitions in existing AI-driven robotic systems and the lack of systematic modeling of the relationship between fairness and user data privacy. The authors propose a utility-aware fairness metric and integrate it with differential privacy mechanisms within a unified optimization framework to achieve fair decision-making while preserving privacy. Innovatively, the approach explicitly links the privacy budget to fairness metrics, revealing that differential privacy mechanisms can synergistically advance fairness objectives. Experimental validation on robotic navigation tasks demonstrates the framework’s effectiveness, showing that compliant privacy-preserving mechanisms can simultaneously satisfy fairness requirements and enhance the trustworthiness of autonomous systems in everyday scenarios.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Bias, Fairness & Privacy
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

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.

Problem

• fairness
• privacy
• robotic applications
• generative AI
• user utility

Innovation

• fairness-aware AI
• privacy-budget fairness trade-off
• utility-aware fairness metric
• generative AI in robotics
• ethical autonomous systems