{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:11:12Z","timestamp":1784131872422,"version":"3.55.0"},"reference-count":415,"publisher":"Emerald","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,24]]},"abstract":"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. We systematically examine 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, we identify persistent challenges and promising research directions in the pursuit of fairer search systems. Our 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.<\/jats:p>","DOI":"10.1561\/1500000101","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T11:01:53Z","timestamp":1735038113000},"page":"262-416","source":"Crossref","is-referenced-by-count":12,"title":["Fairness in Search Systems"],"prefix":"10.1108","volume":"18","author":[{"given":"Yi","family":"Fang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Santa Clara University ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashudeep","family":"Singh","sequence":"additional","affiliation":[{"name":"Microsoft ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Information, Rochester Institute of Technology ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"2026040314420629900_ref001","unstructured":"Abdollahpouri, H., Adomavicius, G., Burke, R., Guy, I., Jannach, D., Kamishima, T., Krasnodebski, J., Pizzato, L.\n 2019, \u201cBeyond personalization: Research directions in multistakeholder recommendation\u201d. arXiv preprintarXiv:1905.01986."},{"key":"2026040314420629900_ref002","unstructured":"Abdollahpouri, H., Burke, R.\n 2019, \u201cMulti-stakeholder recommendation and its connection to multi-sided fairness\u201d. arXiv preprintarXiv:1907.13158."},{"key":"2026040314420629900_ref003","doi-asserted-by":"crossref","DOI":"10.1145\/3109859.3109912","article-title":"\u201cControlling popularity bias in learning-to-rank recommendation\u201d","volume-title":"RecSys","author":"Abdollahpouri","year":"2017"},{"key":"2026040314420629900_ref004","doi-asserted-by":"crossref","unstructured":"Abid, A., Farooqi, M., Zou, J.\n 2021, \u201cLarge language models associate Muslims with violence\u201d. 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