Exposure Fairness

Exposure Fairness refers to the principle and methods in machine learning recommendation systems that ensure each item or user receives a fair chance of exposure. It aims to optimize algorithms to reduce unfair phenomena caused by data bias, enhancing the overall fairness and transparency of the system. Within an adversarial learning framework, Exposure Fairness can effectively balance the interests of different groups, preventing resources from being overly concentrated on a few popular items, thereby improving user experience and satisfaction.