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Search bias quantification: investigating political bias in social media and web search.

, , , , , , and . Inf. Retr. Journal, 22 (1-2): 188-227 (2019)

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Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment., , , and . CoRR, (2016)A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices., , , , , , and . KDD, page 2239-2248. ACM, (2018)Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning., , , and . AAAI, page 51-60. AAAI Press, (2018)Loss-Aversively Fair Classification., , , and . AIES, page 211-218. ACM, (2019)Search bias quantification: investigating political bias in social media and web search., , , , , , and . Inf. Retr. Journal, 22 (1-2): 188-227 (2019)SplitBuff: Improving the interaction of heterogeneous RTT flows on the Internet., , , and . ICC, page 2315-2319. IEEE, (2013)From Parity to Preference-based Notions of Fairness in Classification., , , , and . NIPS, page 229-239. (2017)On the Wisdom of Experts vs. Crowds: Discovering Trustworthy Topical News in Microblogs., , , , and . CSCW, page 437-450. ACM, (2016)Strength in Numbers: Robust Tamper Detection in Crowd Computations., , , , , , , and . COSN, page 113-124. ACM, (2015)Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media., , , , , , and . CSCW, page 417-432. ACM, (2017)