Conference paper Open Access

Addressing Social Bias in Information Retrieval

Jahna Otterbacher


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    <subfield code="x">Otterbacher, J.: Crowdsourcing stereotypes: linguistic bias in metadata generated via gwap. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 1955–1964. ACM, New York (2015). https:// doi.org/10.1145/2702123.2702151</subfield>
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    <subfield code="a">Social biases</subfield>
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    <subfield code="a">This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement  No 739578 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.

This is a pre-print of an article published in Experimental IR Meets Multilinguality, Multimodality, and Interaction 9th International Conference of the CLEF Association, CLEF 2018, Avignon, France, September 10-14, 2018, Proceedings. The final authenticated version is available online at https://www.springer.com/la/book/9783319989310. © Springer Nature Switzerland AG 2018.</subfield>
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    <subfield code="a">Jahna Otterbacher</subfield>
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    <subfield code="a">&lt;p&gt;Journalists and researchers alike have claimed that IR systems are socially biased, returning results to users that perpetuate gender&lt;br&gt;
and racial stereotypes. In this position paper, I argue that IR researchers and in particular, evaluation communities such as CLEF, can and should address such concerns. Using as a guide the Principles for Algorithmic Transparency and Accountability recently put forward by the Association for Computing Machinery, I provide examples of techniques for examining social biases in IR systems and in particular, search engines.&lt;/p&gt;</subfield>
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