A world of sensors gives us almost complete surveillance. Every mobile device tracks moves, forming a digital alibi or new evidence for the prosecution. And with the right data, predictions look frighteningly like guilt.
How does a data-driven, connected world deal with crime, conflict, and peacekeeping? Will we be prisoners in a global Panopticon, begrudgingly honest because we might be surveilled? Or will total transparency even the balance between the enforcer and the citizen?
Join a lineup of thinkers and technologists for this free online event as we look at the ways data is shaping how we police ourselves, from technological innovations to ethical dilemmas.
About Alistair Croll
Alistair has been an entrepreneur, author, and public speaker for nearly 20 years. He's worked on a variety of topics, from web performance, to big data, to cloud computing, to startups, in that time. In 2001, he co-founded web performance startup Coradiant (acquired by BMC in 2011), and since that time has also launched Rednod, CloudOps, Bitcurrent, Year One Labs, the Bitnorth conference, the International Startup Festival and several other early-stage companies.
Alistair is the chair of O'Reilly's Strata conference, Techweb's Cloud Connect, and the International Startup Festival. "Lean Analytics" is his fourth book on analytics, technology, and entrepreneurship. He lives in Montreal, Canada and tries to mitigate chronic ADD by writing about far too many things at "Solve For Interesting".
Adaptive Adversaries: building systems to fight fraud and cyber intruders.
Statistical machine learning techniques tend to fail when faced with an adaptive adversary attempting to evade detection in the data. Humans do an excellent job of correctly spotting adaptive adversaries given a good way to digest the data. On the other hand, humans are glacially slow and error-prone when it comes to moving through very large volumes of data, a task best left to the machines.
Fighting complex fraud and cyber-security threats requires a symbiosis between the computers and teams of human analysts. The computers use algorithmic analysis, heuristics, and/or statistical characterization to find interesting 'simple' patterns in the data. These candidate events are then queued for in-depth human analysis in rich, expressive, interactive analysis environments.
In this talk, we'll take a look at case studies of three different systems, using a partnership of automation and human analysis on large scale data to find the clandestine human behavior that these datasets hold, including a discussion of the backend systems architecture and a demo of the interactive analysis environment.
The backend systems architecture is a mix of open source technologies, like Cassandra, Lucene, and Hadoop, and some new components that bind them all together.
The interactive analysis environment allows seamless pivoting between semantic, geospatial, and temporal analysis with a powerful GUI interface that's usable by non-data scientists.
The systems are real systems currently in use by commercial banks, pharmaceutical companies, and governments.
About Ari Gesher
Ari Gesher is a senior engineer and Engineering Ambassador at Palantir Technologies.
At Palantir Technologies, Ari has split his time between working as a backend engineer on Palantir's analysis platform, thinking and writing about Palantir's vision for human-driven information data systems, and moonlighting on Palantir's Philanthropic engineering team. His current role involves understanding and discussing Palantir's role in the world of analytics, big data, and the future of technology.
An alumnus of the University of Illinois computer science department, Ari has worked in the software industry for the past fifteen years, including a stint as the lead engineer for the SourceForge.net open source software archive.
Big data: Connectivity and professional disaster management
Greater connectivity among citizens through mobile phones and social media is increasing information available in disasters. Sociological barriers, data type and quality, the vast amount of information, and other challenges are outstripping the ability of professional disaster managers to use the information in disaster response for their killer app, which is real-time decision making. We will dive into some of the challenges, existing solutions and opportunities, in this extreme use case to identify lessons applicable for a common general need: real-time sense-making of big data that is robust to disruption and at the limits of current software and hardware.
About Jeannie Stamberger
Jeannie Stamberger is a thought leader for novel solutions in disaster management at intersection of academia, corporations, non-governmental organizations (NGOs) and government agencies (local, federal, domestic and international). She has a doctorate from Stanford, was a key leader in the Carnegie Mellon Silicon Valley Disaster Management Initiative to develop next generation disaster response technology, and most recently was the Director of Innovation of the $25M USAID program "ResilientAfrica Network" to mitigate the need for response to chronic crises by increasing community resilience in sub-Saharan Africa. Her user-center, hi-lo tech approach is reflected in concepts such as "Tweak the Tweet" (in collaboration with EPIC, University of Colorado Boulder) and "ButterflyNet" (BioACT program at Stanford Computer Science Department). She has worked as an adviser, board member, research and consultant, with researchers, government agencies, and start-ups creating products robust to disruption.
Software is Eating Bullets — Using Information to Empower Law Enforcement
A recent report showed that when police officers wear cameras, use of force goes down by 59% and complaints against officers goes down by 87%. This means 59% less punches, bullets, baton uses, etc. Why wasn't this technology adopted sooner? Our public safety agencies are not prepared for the backend management of video, audio and images and subsequently are slow to adopt of beneficial technologies like on-officer video. With cloud services like Evidence.com, the power of rich multimedia data can be used to further improve our public safety efforts. By allowing an off-premise solution to worry about technical scale, law enforcement can focus their efforts on policing rather than scaling massive IT infrastructure.
About Jason Droege
His business unit is responsible for Evidence.com, AXON Flex, and TASER Cam. Jason joined TASER in November 2008 to launch the Video & Cloud business unit for TASER. Previously Jason was President of Gizmo5 Technologies (VoIP company acquired by Google), Founder & President of The Back 9 Golf Co. (think Kelley Blue Book for golf equipment) and Co-founder & VP, Business Development of Scour.com (music, image and video search engine acquired by Centerspan Communications). Primary areas of experience include consumer internet products (Gizmo5 and Scour), ecommerce (The Back 9 Golf Co.) and enterprise software (Evidence.com). Jason attended UCLA where he majored in Computer Science.
Waging Peace with Big Data and New Technologies
Chris Perry, Marie O'Reilly
If big data can be used to predict changes in consumer behavior, can it be used to predict whether rival factions will go to war? Big data analytics, data visualization, and new technologies help the private sector create value for shareholders and consumers. How can international actors, governments, and civil society work with the private sector to leverage these tools for the prevention of violence and conflict?
There has been incremental adoption of new technology and data science in managing violence and conflict. For instance, NGOs in Nairobi made use of crowdsourcing through SMS to track incidents of violence and fraud in the most recent Kenyan elections. Similar efforts in Sudan and South Sudan, however, have run into significant challenges presented by low-tech fragile environments. Further complicating matters, governments in the midst of popular uprisings have increasingly turned to shutting off internet access as a method to quell unrest, making many of the big data collection methods difficult.
Recent research by the International Peace Institute, supported by UNDP and USAID, developed a preliminary guide for applying big data and ICTs to conflict prevention. It throws the gauntlet for more effective partnerships between the public and private sectors—partnerships that must begin with a common understanding of both the challenges and possibilities.
In parallel, over the past year the International Peace Institute's Data Lab has worked to demonstrate the uses of data science for conflict mapping and policy research. Join us as we examine some of the successes and failures of new technology in the field of peace and conflict and explore the prospects and challenges of adapting data science to policy research.
About Chris Perry
Chris joined IPI as a Policy Analyst with the Coping with Crisis team in July 2008. His current work deals with a range of international peace and security issues through the lens of multilateralism. His research focuses on applying data science to problems of sustainable development, conflict mediation and prevetion, and peacekeeping. His most recent work involved mining UN archival documents to create a database of UN Peacekeeping contributions over time, which can be used to test hypotheses regarding the motivations for contributing to UN peacekeeping missions. Chris holds a master's in public administration from the Daniel J. Evans School (University of Washington) with a concentration in international development policy and a BA in philosophy from the University of Washington.
About Marie O'Reilly
Marie O'Reilly is the Associate Editor at the International Peace Institute (IPI) and a co-author of the IPI report New Technology and the Prevention of Violence and Conflict. Prior to joining IPI, Marie conducted research on the impact of natural resources on peacebuilding in South Sudan for the United Nations Department of Peacekeeping Operations and developed conflict-prevention strategies targeting youth for the United Nations Development Programme in Lebanon. She has also worked in management consulting with Accenture, on human rights and video advocacy with Witness, and on event planning and communications at the Center for International Studies and Research (CERI) in Paris.
Re-thinking conflict early warning: big data and systems thinking
Helena Puig Larrauri
Research on the use of big data to build predictive models of conflict is advancing. Statistical models using large event datasets, machine-learning techniques to perfect prediction and analysis of tone in social media are all important contributions. However, a key question remains unanswered: how can the predictions that these models offer help peacebuilders in the field? In other words, is early warning of a conflict spike the type of information that peacebulders needs to take action, respond faster or intervene more effectively? Big data offers a rich source of interactions between and opinions expressed by different actors in a conflict setting. It's akin to listening to the social hubbub of the "Track Two" in peace negotiations. Could we use big data analysis tools to listen to social hubbub - not to predict conflict spikes but to better understand the intervention points in complex conflict dynamics?
UNDP is exploring a number of these questions by conducting a series of case studies that make use of the Global Data on Events, Location and Tone. This work points the way for future applied research to make big data analysis accessible and useful to peacebuilders.
About Helena Puig Larrauri
Helena Puig Larrauri is a peacebuilding practitioner, focusing on the use of technology to promote peace and prevent conflict. She is currently a freelance consultant, working on projects with non-governmental and United Nations agencies in conflict and post-conflict environments including Sudan, Libya, Cyprus, Zimbabwe, Nepal, Kyrgyzstan and Iraq. She works 50% for Mercy Corps as an advisor on youth and conflict programming. Helena is an organizer of Build Peace and co-founder of YoLab. She is also on the Board of Advisors of the Standby Task Force, an online volunteer technical community for crisis response that she co-founded in 2010. She holds a Master in Public Affairs (Economics) from Princeton University's Woodrow Wilson School and a Bachelor's degree from Oxford University (where she also served as Student Union president for one year). She blogs at http://letthemtalk.org/
Mobile Phone Data for Understanding Population Displacement after Disasters
On average, 30 million people are displaced every year by natural disasters. Millions more are displaced by famines and conflict. During large disasters and crises there is a severe lack of basic information on the locations of affected people. This prevents relief organizations from delivering the right amounts of supplies to the right places, even when sufficient resources are available. Researchers at the Flowminder Foundation have pioneered the use of mobile phone operator data to understand these movements and support relief agencies in disaster response. Dr. Bengtsson will talk about his research and field work in Haiti, Cote D'Ivoire and Bangladesh, as well as the caveats to take into account in using operator data to track population displacement.
About Linus Bengtsson
Linus Bengtsson is a medical doctor, co-founder of the Flowminder Foundation and researcher at Karolinska Institute in Stockholm. He initiated and led the work to monitor population displacement using mobile phone operator data in Haiti after the 2010 earthquake and cholera outbreak. His academic research centres on the development of public health applications of big data and information technology in low- and middle-income countries. He has previously been working as Clinical Epidemiologist for the Gapminder Foundation and has a PhD in Global Public Health from Karolinska Institute and an MD from Gothenburg University. Dr. Bengtsson has conducted surveys and interventions using online and mobile networks in Vietnam and Haiti, and has additional field experience from Burkina Faso, Bangladesh, Cote d'Ivoire, Pakistan, Brazil, and Ethiopia.