Machine learning (ML) is essential in modern technology, driving complex data-driven decisions. By 2025, daily data generation will exceed 463 exabytes, increasing ML’s influence and ethical risks of data exploitation and discrimination. The European Union’s Artificial Intelligence Act highlights the need for ethical AI solutions.

Project FRINGE (context-aware FaiRness engineerING in complex software systEms) addresses software fairness in ML-intensive systems that collect data through interconnected devices. FRINGE aims to provide software engineers, data scientists, and ML experts with methodologies and software engineering solutions to improve fairness in ML systems. The goals of the project include developing a metamodel for ML fairness, a fairness-aware monitoring infrastructure, contextual solutions for identifying fairness issues, and automated recommendation systems to design fairness properties throughout the software development lifecycle.