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STSM – Algorithmic Biases and Digital Divides
April 2, 2016 - April 9, 2016
The explosion of digital media has led to an increasing reliance on information retrieval (and recommendation) algorithms. From Web search to sharing in social media platforms, members of the public rely on retrieval and filtering mechanisms to make sense of large volumes of content. However, most users have little idea how the content they see has been organized, and what or who is missing from the content as presented to them. The biases within online services, such as Web search engines, will affect an individual’s perception of what online information is available and where (see, http://www.techrepublic.com/blog/it-security/search-engine-bias-what-search-results-are-telling-you-and-what-theyre-not/). The goal of this collaborative project will be to investigate a range of online systems, in particular Web search engines, using various qualitative and quantitative methods to investigate (search-engine) bias. In particular we are interested in the biases
that are displayed for different types of user, such as those with varying degrees of digital literacy skill or language ability.
A key theme to be explored is the objectivity and accessibility of search engine results concerning topics of interest to users worldwide (e.g., healthcare, environmental conservation). How “objective” are the results presented in terms of content and sources, are some search engines more biased than others, and how might we measure objectivity? While we plan to begin our study within the contexts of search engines, this can later be generalized to other contexts such as algorithmic curation in social media. This also fits broadly within the emerging area of “Data Power” as evidenced in a recent conference at Sheffield (http://www.sheffield.ac.uk/socstudies/datapower).
The main deliverable will be the development of a methodology to explore the topic, initial data collection and the beginnings of a publication for 2016-2017. In addition, possible leads for funding our work will be explored, such as the upcoming Horizon 2020 WIDESPREAD Teaming call (November 2016), which pairs up institutions in low performing EU Member States (e.g., Cyprus) with institutions of research and innovation excellent, such as Sheffield.
Jahna Otterbach (University of Cyprus) visits the Information School at Sheffield University.