Author Description

Search engines play a crucial role in organizing and delivering information to billions of users worldwide. However, these systems often reflect and amplify existing societal biases and stereotypes through their search results and rankings. This concern has prompted researchers to investigate methods for measuring and reducing algorithmic bias, with the goal of developing more equitable search systems. This monograph presents a comprehensive taxonomy of fairness in search systems and surveys the current research landscape. This work systematically examines how bias manifests across key search components, including query interpretation and processing, document representation and indexing, result ranking algorithms, and system evaluation metrics. By critically analyzing the existing literature, persistent challenges and promising research directions in the pursuit of fairer search systems are identified. The aim is to provide a foundation for future work in this rapidly evolving field while highlighting opportunities to create more inclusive and equitable information retrieval technologies.