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The Web Science and Social Computing Group at Universitat Pompeu Fabra in Barcelona, which I lead, is inviting applications for PhD students interested in crisis informatics and algorithmic discrimination, and in general on social computing applications that address issues of social significance.

The crisis informatics student will seek to deepen our understanding of how community-generated content in social platforms can be used to improve emergency/disaster response, and to improve the resilience of societies. A student willing to pursuit this topic must have an orientation to practical problems, excellent programming skills, and be motivated to create new applications to use time-sensitive, sometimes life-saving data.

The algorithmic discrimination student will seek to deepen our understanding of how algorithms can embody and sometimes exacerbate biases against less advantaged groups in society. A student willing to pursuit this topic must have an excellent background on statistics, data mining, or machine learning, good data management skills, and be motivated to work in applications for education, justice, medicine, and other areas in which algorithms are used in the public sector.

Both positions are funded through DTIC fellowships.

For more information, visit Web Science and Social Computing » PhD Opportunities.

Extreme weather events put the most vulnerable communities at high risk.

How can data analytics strengthen early warning systems and and support relief efforts for communities in need?

The size and frequency of natural disasters is growing at an alarming pace. In 2016 earthquakes, wildfires and other natural events caused US$210bn in global economic losses, according to a UK-based insurance broker, Aon. The year 2017 may tally an even higher figure, as a series of floods, earthquakes and hurricanes struck various areas of the world.

Developing economies, especially those located closer to the equator, are expected to bear the greatest toll from extreme weather events. These countries are the most vulnerable and least equipped to withstand these types of events, as they have fewer resources to prevent damage and protect citizens who are at risk.

Data and analytics can support relief and response initiatives for communities in need. From the use of satellite images and crowd-sourced mapping tools to predict and help prepare for disasters, to on-the-ground reports from drone footage, emergency responders, governments and non-government organisations (NGOs) are adopting data analytics as a critical tool to strengthen early warning systems and aid relief efforts in the aftermath of a disastrous event.

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Decisions that are partially or completely based on the analysis of large datasets are becoming more common every day. Data-driven decisions can bring multiple benefits, including increased efficiency and scale. Decisions made by algorithms and based on data also carry an implicit promise of "neutrality." However, this supposed algorithmic neutrality has been brought into question by both researchers and practitioners.

Algorithms are not really "neutral." They embody many design choices, and in the case of data-driven algorithms, include decisions about which datasets to use and how to use them. One particular area of concern are datasets containing patterns of past and present discrimination against disadvantaged groups, such as hiring decisions made in the past and containing subtle or not-so-subtle discriminatory practices against women or minority races, to name just two main concerns. These datasets, when used to train new machine-learning based algorithms, can contribute to deepen and perpetuate these disadvantages. There can be potentially many sources of bias, including platform affordances, written and unwritten norms, different demographics, and external events, among many others.

The study of algorithmic fairness can be understood as two interrelated efforts: first, to detect discriminatory situations and practices, and second, to mitigate discrimination. Detection is necessary for mitigation and hence a number of methodologies and metrics have been proposed to find and measure discrimination. As these methodologies and metrics multiply, comparing across works is becoming increasingly difficult.

We have created a new website, where we would like to collaborate with others to create benchmarks for algorithmic fairness. To start, we have implemented a number of basic and statistics measures in Python, and prepared several example datasets so the same measurements can be extracted across all of them.

We invite you to check the data and code available in this website, and let us know what do you think. We would love to hear your feedback: http://fairness-measures.org/.

Contact e-mail: Meike Zehlike, TU Berlin meike.zehlike@tu-berlin.de.

Meike Zehlike, Carlos Castillo, Francesco Bonchi, Ricardo Baeza-Yates, Sara Hajian, Mohamed Megahed (2017): Fairness Measures: Datasets and software for detecting algorithmic discrimination. http://fairness-measures.org/

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.