Machine Learning and the Humanitarian Information Gap
Dr John Quinn, United Nations Global Pulse, UK
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.
Differential privacy and how it compares with legal standard of privacy
Professor Kobbi Nissim
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.
Data science for the public sector
Professor Slava Mikhaylov
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.
The automation of political communication on Twitter: the case of the Brexit botnet
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.