09:00-09:30
Machine Learning and the Humanitarian Information Gap
Dr John Quinn, United Nations Global Pulse, UK
Abstract
Mounting an effective response to a humanitarian crisis depends on high quality and timely information. However, the very nature of such crises makes it a challenge to collect reliable data, particularly in the time scale of days or hours when it is most needed. Given the unprecedented quantities of data now being generated worldwide (e.g. by sensors, satellites, mobile devices, and the usage of digital services), as well as recent advances in the algorithms which can make sense of this raw data, there is significant potential to improve the initial assessment and ongoing monitoring of emergencies. This talk will discuss some of the opportunities and limitations, using examples of work conducted during various natural and man-made emergencies.
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Dr John Quinn, United Nations Global Pulse, UK
Dr John Quinn, United Nations Global Pulse, UK
John Quinn is a Data Scientist at UN Global Pulse, dealing primarily with analytics projects in Africa, where he has been technical lead on a number of large-scale initiatives. From 2007 to 2015 he was a faculty member of the Department of Computer Science in Makerere University, Uganda. His research interests are in artificial intelligence and data science, and the application of these to practical problems in the developing world. He received a BA in Computer Science from the University of Cambridge in 2000, and a PhD in machine learning from the University of Edinburgh in 2007.
09:45-10:15
Differential privacy and how it compares with legal standard of privacy
Professor Kobbi Nissim
Abstract
Differential privacy is a robust concept of privacy which brings mathematical rigor to the decades-old problem of privacy-preserving analysis of collections of sensitive personal information. Informally, differential privacy requires that the outcome of an analysis would remain stable under any possible change to an individual's information, and hence protects individuals from attackers that try to learn the information particular to them. The subject of much theoretical investigation, differential privacy has recently been making significant strides towards implementation and use.
This talk will present differential privacy and discuss how one can reason about how it matches with concepts of privacy appearing in privacy law and regulations.
Based on the work of a working group: K Nissim, A Bembenek, A Wood, M. Bun, M Gaboardi, U Gasser, D O'Brien, T Steinke, and S Vadhan.
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Professor Kobbi Nissim
Professor Kobbi Nissim
Professor Kobbi Nissim is McDevitt Chair in Computer Science, Georgetown University. Nissim’s work is focused on the mathematical formulation and understanding of privacy. His work from 2003 and 2004 with Dinur and Dwork initiated rigorous foundational research of privacy and presented a precursor of Differential Privacy - a definition of privacy in computation that he introduced in 2006 with Dwork, McSherry and Smith. His research studies privacy in various contexts, including statistics, computational learning, mechanism design, social networks, and more recently law and policy. Since 2011, Nissim has been involved with the Privacy Tools for Sharing Research Data project at Harvard, developing privacy-preserving tools for the sharing of social-science data. Nissim was awarded the Godel Prize In 2017, the IACR TCC Test of Time Award in 2016, and the ACM PODS Alberto O Mendelzon Test-of-Time Award in 2013.
11:00-11:30
Data science for the public sector
Professor Slava Mikhaylov
Abstract
Public sector organisations are increasingly interested in using data science capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science in the public sector relies on successfully embedding it into delivery solutions for policy implementation. This requires organisational innovation and change delivered through structural and cultural adaptation, together with capacity building. Another key factor for success is the contribution of academia and the private and third sector. This talk will discuss the opportunities that exist for using data science in delivering public services at the international and national levels.
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Professor Slava Mikhaylov
Professor Slava Mikhaylov
Slava Mikhaylov is a Professor of Public Policy and Data Science at the University of Essex, holding a joint appointment in Department of Government and Computer Science Department Institute for Analytics and Data Science. He is a Chief Scientific Adviser to Essex County Council and a co-investigator in the UK Economic and Social Research Council Big Data infrastructure investment initiative – Consumer Data Research Centre at University College London. His research and teaching is primarily in the field of machine learning and natural language processing.
11:45-12:15
The automation of political communication on Twitter: the case of the Brexit botnet
Abstract
Dr Dan Mercea, City, University of London, UK
This presentation reports on a network of Twitterbots— automatic posting protocols—comprising 13,493 accounts that tweeted the U.K. E.U. membership referendum, only to disappear from Twitter shortly after the ballot. We compared active users to this set of political bots with respect to temporal tweeting behaviour, the size and speed of retweet cascades, and the composition of their retweet cascades (user-to-bot vs. bot-to-bot) to evidence strategies for bot deployment. Our results move forward the analysis of political bots by showing that Twitterbots can be effective at rapidly generating small to medium-sized cascades; that the retweeted content comprises user-generated hyperpartisan news, which is not strictly fake news, but whose shelf life is remarkably short; and, finally, that a botnet may be organized in specialized tiers or clusters dedicated to replicating either active users or content generated by other bots.