Blogs

With the support of a Data Transparency Lab grant, working with Meike Zehlike and Tom Sühr from TU Berlin, and Ivan Kitanovski from Ss. Cyril and Methodius University of Skopje, we have produced new tools for creating fair rankings.

Reference for both tools:
Meike Zehlike, Tom Sühr, Carlos Castillo, Ivan Kitanovski: "FairSearch: A Tool For Fairness in Ranked Search Results". arXiv:1905.13134 (2019). Homepage: https://github.com/fair-search

FA*IR: fair ranking by post-processing

The first set of tools correspond to the FA*IR paper in CIKM 2017, which describes a method for ranking post-processing based on a statistical test called the ranking group fairness condition:

• FA*IR library in Python
• FA*IR library in Java
• FA*IR plug-in for Elasticsearch

Reference for the FA*IR algorithm:
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates: "FA*IR: A Fair Top-k Ranking Algorithm". Proc. of the 2017 ACM on Conference on Information and Knowledge Management (CIKM).

DELTR: fair ranking in-processing by learning-to-rank

The second set of tools correspond to an unpublished work on Learning To Rank (LTR) while reducing disparate impact, an in-processing algorithm named DELTR:

• DELTR library in Python
• DELTR library in Java
• DELTR adapter for Elasticsearch

Reference for the DELTR algorithm:
Meike Zehlike, Gina-Theresa Diehn, Carlos Castillo. "Reducing Disparate Exposure in Ranking: A Learning to Rank Approach" arXiv:1805.08716 (2018).

The European Commission's Joint Research Center is working on a tool that could use tweets and artificial intelligence to collect real-time data on floods. In a paper released on Arvix.org, EU scientists explain how their Social Media for Flood Risk (SMFR) prototype could help emergency responders better understand what's happening on the ground in flooded areas and determine what trouble spots might need immediate attention.

The tool works in collaboration with Europe's Flood Awareness System (EFAS). When EFAS identifies areas with heightened flood risks, it triggers SMFR to begin collecting flood-related tweets from users in those areas. Gathering reliable information from Twitter is no easy task, especially considering that EFAS covers an area with more than 27 languages. That's where the team put AI to work. To start, the researchers trained SMFR to spot flood-related keywords in English, German, Spanish and French. In a test during floods in Calabria, Italy, last fall, the tool successfully gathered 14,347 tweets over three days, sorted them by relevance and provided geo-location data.

Continue reading in Engadget »

Valerio Lorini, Carlos Castillo, Francesco Dottori, Milan Kalas, Domenico Nappo, Pater Salamon: Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach. To appear in ISCRAM. Valencia, Spain. [arxiv]

1. Ghost in the Shell Soundtrack - Making of a Cyborg
2. Fatima Yamaha - The Creature from Culture Creation
3. Panthera Krause - If (Benjamin Frohlich Basic refix)
4. Pet Shop Boys - Love Etc (Gui Boratto Remix)
5. Guy J - Transitions (Komaroff Remix)
6. Perel - Alles (Red Axes Remix)
7. Kollektiv Turmstrasse - Ordinary (Lake People's Circle Motive Remix)
8. Agoria - Singing
9. Closer Musik - Maria
10. Big Wild - When I Get There

This trimester I'm giving for the first time a course on Introduction to Network Science for the undergraduate studies on Data Science at Universitat Pompeu Fabra.

• Why studying networks
• Basics concepts of graph theory
• Random networks
• Scale-free networks
• Preferential attachment
• Link formation mechanisms
• Homophily and assortativity
• Hubs and authorities
• PageRank
• Link-based centrality
• Network flows
• Dense sub-graphs
• Hierarchical clustering
• Spreading phenomena

All the materials of the course are available on GitHub: https://chatox.github.io/networks-science-course/

IEEE Intelligent System is hosting a special issue on AI for Disaster Management and Resilience, which we are co-editing with Yu-Ru Lin from University of Pittsburgh, USA and Jie Yin from University of Sydney, Australia.

• Submission: November 15, 2018
• Notification of acceptance: April 30, 2019

In recent years, there have been an increasing number of large-scale crises, such as natural disasters or armed attacks, that have had major effect on individual lives and infrastructure, and have caused the devastation to communities. During these mass emergencies, victims, responders, and volunteers increasingly use social media and mobile devices to provide real-time situation updates, i.e., reports on damage, or request and offer help. This has generated vast volumes of crisis data in different forms and from different sources. There are a number of challenges associated with near-real-time processing of vast volumes of information in a way that makes sense for people directly affected, for volunteer organizations, and for official emergency response agencies. There is a growing need for developing new AI techniques that process large-scale crisis data to gain a “big picture” of an emergency, detect and predict how a disaster could develop, analyze the impact of disasters and the effect of negative externalities in a cyber-physical society, and assist in disaster response and resource allocation. These AI techniques can allow better preparation for emergency situations, help save lives, limit economic impact, provide effective disaster relief, and make communities stronger and more resilient.

This special issue is to call for research initiatives toward the next generation disaster management that leverage AI to strengthen disaster resilience at all levels of society in the new age of mass emergencies.

Read more information on the call for papers »