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New grant by the Data Transparency Lab for a tool for fair rankings in search

New grant by the Data Transparency Lab for a tool for fair rankings in search

The Data Transparency Lab (DTL) will fund 6 projects in its 2017 call with a 50,000 euro grant to support a new breed of transparency software. FA*IR: A tool for fair rankings in search, proposed by the team composed by Meike Zehlike (Technische Universität Berlin), Francesco Bonchi (ISI Foundation // Eurecat), Carlos Castillo (Eurecat // UPF), Sara Hajian (Eurecat), Ricardo Baeza-Yates (UPF) and Odej Kao (Technische Universität Berlin), is among the recipients of this initiative sponsored by entities such as AT&T, INRIA, IPVanish, MIT Connection Science, Mozilla, privateinternetacces, RedMorph and Telefónica.

28.07.2017

The Data Transparency Lab (DTL) will fund 6 projects in its 2017 call with a 50,000 euro grant to support a new breed of transparency software. FA*IR: A tool for fair rankings in search, proposed by the team composed by Meike Zehlike (Technische Universität Berlin), Francesco Bonchi (ISI Foundation // Eurecat), Carlos Castillo (Eurecat // UPF), Sara Hajian (Eurecat), Ricardo Baeza-Yates (UPF) and Odej Kao (Technische Universität Berlin), is among the recipients of this initiative sponsored by entities such as AT&T, INRIA, IPVanish, MIT Connection Science, Mozilla, privateinternetacces, RedMorph and Telefónica.

FA*IR: A tool for fair rankings in search

People search engines, for example, are increasingly common for job recruiting, for finding a freelancer, and even for finding companionship or friendship. As in similar cases, a top-k ranking algorithm is used to find the most suitable way of shortlisting and ordering the items (persons, in this case), considering that if the number of candidates matching a query is large, most users will not scan the entire list. Conventionally, these lists are ranked in descending order of some measure of the relative quality of items (e.g. years of experience or education, up-votes, or inferred attractiveness). Unsurprisingly, the results of these ranking and search algorithms potentially have an impact on the people who are ranked, and contribute to shaping the experience of everybody online and offline. Due to its high importance and impact, our aim is to develop the first fair open source search API. This fair ranking tool will enforce ranked group fairness, ensuring that all prefixes of the ranking have a fair share of items across the groups of interest, and ranked individual fairness, reducing the number of cases in which a less qualified or lower scoring item is placed above a more qualified or higher scoring item. We will create this fair search API by extending a popular, well-tested open source search engine: Apache Solr. We will develop this search API considering both the specific use case of people search, as well as considering a general-purpose search engine with fairness criteria. Taking a long-term view, we believe the use of this tool will be an important step towards achieving diversity and reducing inequality and discrimination in the online world, and consequently in society as a whole.

More info: FA*IR: A Fair Top-k Ranking Algorithm (arXiv)

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