Fairness Audit

Fairness auditing assesses whether machine learning models and decision-making systems exhibit bias against specific subgroups, aiming to ensure equitable outcomes. Current research focuses on addressing challenges like unobserved confounding factors, improving the efficiency of audits through multi-agent collaboration and novel sampling techniques, and developing methods for privacy-preserving audits. This field is crucial for mitigating algorithmic bias in high-stakes applications like healthcare and criminal justice, promoting responsible AI development, and fostering trust in automated systems.