@article{zehlike_fairsearch:_2019,
	title = {{FairSearch}: A Tool For Fairness in Ranked Search Results},
	url = {http://arxiv.org/abs/1905.13134},
	shorttitle = {{FairSearch}},
	abstract = {Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, rankings may determine career and business opportunities, educational placement, access to benefits, and even social and reproductive success. It is therefore of societal and ethical importance to ask whether search results can demote, marginalize, or exclude individuals of unprivileged groups or promote products with undesired features. In this paper we present {FairSearch}, the first fair open source search {API} to provide fairness notions in ranked search results. We implement two algorithms from the fair ranking literature, namely {FA}*{IR} (Zehlike et al., 2017) and {DELTR} (Zehlike and Castillo, 2018) and provide them as stand-alone libraries in Python and Java. Additionally we implement interfaces to Elasticsearch for both algorithms, that use the aforementioned Java libraries and are then provided as Elasticsearch plugins. Elasticsearch is a well-known search engine {API} based on Apache Lucene. With our plugins we enable search engine developers who wish to ensure fair search results of different styles to easily integrate {DELTR} and {FA}*{IR} into their existing Elasticsearch environment.},
	journaltitle = {{arXiv}:1905.13134 [cs]},
	author = {Zehlike, Meike and Sühr, Tom and Castillo, Carlos and Kitanovski, Ivan},
	urldate = {2019-07-10},
	date = {2019-05-27},
	eprinttype = {arxiv},
	eprint = {1905.13134},
	keywords = {Computer Science - Information Retrieval, H.3.3}
}

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