Fairness Guarantee

Fairness guarantees in machine learning aim to mitigate biases and discrimination in algorithmic decision-making, focusing on ensuring equitable outcomes across different demographic groups and individuals. Current research explores various approaches, including data transformation techniques (e.g., using normalizing flows), leveraging ambiguity in sensitive attributes, and developing differentially private and stable ranking algorithms that consider both group and individual fairness. These advancements are crucial for building trustworthy and ethical AI systems, impacting fields ranging from hiring and loan applications to recommender systems and online platforms.