Conference paper Open Access

Addressing Social Bias in Information Retrieval

Jahna Otterbacher


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Journalists and researchers alike have claimed that IR systems are socially biased, returning results to users that perpetuate gender<br>\nand 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.</p>", 
  "license": "https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Open University of Cyprus, Nicosia,Cyprus and Research Centre on Interactive Media Smart Systems and Emerging Technologies, Nicosia, Cyprus", 
      "@id": "https://orcid.org/0000-0002-7655-7118", 
      "@type": "Person", 
      "name": "Jahna Otterbacher"
    }
  ], 
  "headline": "Addressing Social Bias in Information Retrieval", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2018-09-14", 
  "url": "https://zenodo.org/record/2671635", 
  "version": "Accepted pre-print", 
  "@type": "ScholarlyArticle", 
  "keywords": [
    "Social biases", 
    "Ranking algorithms", 
    "Crowdsourcing"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1007/978-3-319-98932-7_11", 
  "@id": "https://doi.org/10.1007/978-3-319-98932-7_11", 
  "workFeatured": {
    "url": "http://clef2018.clef-initiative.eu/", 
    "alternateName": "CLEF 2018", 
    "location": "Avignon, France", 
    "@type": "Event", 
    "name": "9th International Conference of the CLEF Association,, ,"
  }, 
  "name": "Addressing Social Bias in Information Retrieval"
}
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