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

TL;DR

This paper introduces a utility-aware fairness metric for AI-driven robotic decision-making, analyzing how privacy constraints influence fairness, and demonstrates that privacy budgets can be used to meet fairness targets in robotic applications.

Contribution

It proposes a unified framework linking fairness and privacy in robotic AI, providing a new metric and analysis that guide ethical and trustworthy autonomous systems.

Findings

Privacy budgets can be used to achieve fairness targets.

The framework formalizes the interplay between fairness and privacy.

Tested in a robot navigation task, showing practical applicability.

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.