Biases and Inaccuracies in Web Search Outputs and their Implications
Web search outputs are generally trusted by the public. The selection and ranking of search results are often framed and perceived as “unbiased” and “science-based.” However, more and more studies show that this is not the case and search outputs do not often contain various forms of biases and inaccuracies. This talk is going to be centred around the discussion of such biases and inaccuracies in search outputs as well as the methodological approaches that can be used for their detection, such as algorithm impact auditing. In addition, the implications of such search “malperformance” for individual opinion formation processes will be discussed in the context of survey-based and experimental research.
About the speaker
Aleksandra Urman is a Postdoctoral Researcher at Social Computing Group, University of Zurich, Switzerland. Currently, she studies the impact of algorithmic content curation on social media and search engines with a particular focus on various forms of bias and misrepresentation. In her work, Aleksandra combines computational methods with social science-based perspectives and knowledge.
If you have any questions about the event or want to receive an invitation, please reach out to Marieke van Hoof (firstname.lastname@example.org).