Fairness risk and its privacy-enabled solution in AI-driven robotic applications
This addresses fairness and privacy issues for AI-driven robotic systems, with incremental contributions in combining these aspects.
The paper tackles fairness concerns in generative AI-driven robotic applications by introducing a utility-aware fairness metric and analyzing its interplay with user-data privacy, showing that privacy budgets can be used to meet fairness targets in a robot navigation task.
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.