Bias on the web

By Abbe Mowshowitz, Akira Kawaguchi. CACM, 2002

320 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0733d44e3d88f13e299e3fb6018bfe8aeb3d3546

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In Information Systems

In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

Measuring search engine bias (2005, methodology): “The results of earlier experiments have been reported in Mowshowitz and Kawaguchi (2002b) .”
Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, background): “activity bias A type of selection bias that occurs when systems/platforms get their training data from their most active users, rather than those less active (or inactive) [133].”
Beliefs and biases in web search (2013, background): “Research on result diversity (e.g., [25][40][48]) is also relevant but typically focuses on aspects such as topical variance, rather than results skewed toward one perspective or biased search behaviors.”
Toward a theory of network gatekeeping: A framework for exploring information control (2008, unknown): “…2003; Broder et al., 2000; Dimitrova et al., 2003; Elkin-Koren, 2001; Hargittai, 2000a, 2000b; Introna & Nissenbaum, 2000; Lawrence & Giles, 1999; Mowshowitz & Kawaguchi, 2002; Rogers, 2005; Zittrain & Edelman, 2002) Censorship mechanisms (e.g., filtering, blocking, zoning, and deletion of…”
Predicting Factuality of Reporting and Bias of News Media Sources (2018, background): “…news media has often been estimated automatically based on the general stance of the target medium with respect to known manually fact-checked claims, without access to gold labels about the overall medium-level factuality of reporting (Mukherjee and Weikum, 2015; Popat et al., 2016, 2017, 2018).”
Quantifying Biases in Online Information Exposure (2018, background): “However, we ﬁnd no support for the previously claimed superlinear behavior ( γ > 1 ) (Cho & Roy, 2004; Introna & Nissenbaum, 2000; Mowshowitz & Kawaguchi, 2002) that would be consistent with H3.”
Manipulating SGD with Data Ordering Attacks (2021, background): “Recent work has highlighted that ML models can suffer from a large taxonomy of different biases, including sampling bias, with outcomes including sexism and racism [3, 23].”
Topical interests and the mitigation of search engine bias (2005, background): “We will also refer to this quantity as the traﬃc to the same page.”
Toward a semantic granularity model for domain-specific information retrieval (2011, background): “Unfortunately, the correlation between popularity and relevance could be weak for newly created Web pages with few inlinks [Mowshowitz and Kawaguchi 2002].”
Exploring Artist Gender Bias in Music Recommendation (2020, background): “which details the results obtained. Lastly, in section 7 conclusions and future work are discussed. 2 RELATED WORK The notion of bias has been extensively explored in the Information Retrieval domain [4, 5, 7, 11, 24]. Typically, metrics aim to capture relative bias (i.e. bias pre-existing in data, ”
Comparative study on methods of detecting research fronts using different types of citation (2009, methodology): “Metasearching has been shown to effectively supportWeb searching in a non-English setting (Chung, 2006, 2008) and to providehigherqualityresultsratherthanrelyingononlyafew search engines ( Mowshowitz & Kawaguchi, 2002 ).”