Data Transparency Lab names our project on fair rankings as one of their grantees for 2017

The Data Transparency Lab has awarded our project "FA*IR: A tool for fair rankings in search" one of their grants for the year 2017. The grant will enable the development of an open source API implementing fair ranking methods within a widely-used search engine (Apache SOLR).

People search engines 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.

The DTL grant was awarded to Meike Zehlike (Technische Universität Berlin), Francesco Bonchi (ISI Foundation and Eurecat), Carlos Castillo (Eurecat), Sara Haijan (Eurecat), and Odej Kao (Technische Universität Berlin). Together with Ricardo Baeza-Yates (NTENT) and Mohammed Megahed (Technische Universität Berlin), we have been doing joint research on fair top-k ranking. Some of our results can be found on arXiv pre-print 1706.06368.

More details: DTL Grantees 2017 announced.