long-term

We propose a simulation-based framework called FairSense to detect and analyze long-term unfairness in ML-enabled systems.

This project investigates how feedback loops in ML systems can cause long-term, harmful impacts, and develops tools to detect, analyze, and prevent them before deployment.

We highlight the safety risks posed by feedback loops in ML-based systems, discuss why they are hard to detect with current software engineering methods, and propose research directions to better identify, monitor, and mitigate these effects.