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National and Transnational Security Implications of Big Data in the Life Sciences Prepared by the American Association for the Advancement of Science in conjunction with the Federal Bureau of Investigation and the United Nations Interregional Crime and Justice Research Institute National and Transnational Security Implications of Big Data in the Life Sciences National and Transnational Security Implications of Big Data in the Life Sciences A Joint AAAS-FBI-UNICRI Project 1 | P a g e Acknowledgements About AAAS We would like to thank all the members of the The American Association for the Advancement working group and experts group, who provided of Science (AAAS) is the world’s largest general excellent guidance, advice, and feedback scientific society and publisher of the journal, throughout the project. This project was Science (www.sciencemag.org). AAAS was supported by a contract from the Biological founded in 1848, and serves 262 affiliated Countermeasures Unit of the Federal Bureau of societies and academies of science, reaching 10 Investigation's WMD Directorate. million individuals. Science has the largest paid circulation of any peer-reviewed general science Disclaimer journal in the world, with an estimated total The concerns or suggestions outlined in this readership of one million. The non-profit AAAS report reflect the discussions at the workshop (www.aaas.org) is open to all and fulfills its and do not necessarily represent the views of the mission to “advance science and serve society” FBI WMD Directorate; AAAS Board of through initiatives in science policy, Directors, its Council, or membership; and international programs, science education, and UNICRI. more. Produced in the United States (2014) About UNICRI American Association for the Advancement of The United Nations Interregional Crime and Science Justice Research Institute (UNICRI) was 1200 New York Avenue, NW established by the UN Economic and Social Washington, DC 20005 Council in 1968 to assist the international community in the formulation and implementation of effective policies in the About FBI/WMDD/BCU field of crime prevention and criminal The FBI’s Weapons of Mass Destruction justice. UNICRI' s goals are: 1) to advance Directorate (WMDD) was created after understanding of crime-related problems; 2) September 11, 2001 to provide a cohesive and to foster just and efficient criminal justice coordinated approach to countering WMD systems; 3) to support the respect of threats and responding to incidents if they occur. international instruments and standards; 4) to Recognizing the unique and inherent challenges facilitate international law enforcement to preventing bioterrorism, the cooperation and judicial assistance. FBI/WMDD/Biological Countermeasures Unit UNICRI’s mission is to advance security, (BCU) conducts extensive outreach to the life serve justice and build peace in support of sciences community to proactively build the rule of law, human rights protection and mutually beneficial relationships and broaden sustainable development. UNICRI’s main scientists’ understanding of biosecurity objective is to make a meaningful concerns. contribution in the development of a sustainable platform for information exchange and effective policies, and interventions. 2 | P a g e Experts and Staff Ketan Paranjape, M.S., MBA Working Group Members Intel Satnam Alag, Ph.D. Erik Prentice, Ph.D. Illumina Proactive Worldwide, Inc. Kavita M. Berger, Ph.D., chair Nathan Price, Ph.D. American Association for the Advancement Institute for Systems Biology of Science Charles Schmitt, Ph.D. Tanya Berger-Wolf, Ph.D. Renaissance Computing Institute University of Chicago Matt Wood, Ph.D. Roger Brent, Ph.D. Amazon Web Services Fred Hutchinson Cancer Research Center SSA Edward You, M.S., chair Susan A. Ehrlich, J.D., LL.M. Federal Bureau of Investigation (Biotechnology and Genomics) Judge (ret.), Arizona Court of Appeals Jaroslaw Zola, Ph.D. University at Buffalo, SUNY Steve Evans, Ph.D. Dow AgroSciences Experts Andrew Hessel Autodesk, Inc. Gaymon Bennett, Ph.D. Fred Hutchinson Cancer Research Center Margaret Kosal, Ph.D. Georgia Institute of Technology Mark Greaves, Ph.D. Pacific Northwest National Laboratory Francesco Marelli, Ph.D. United Nations Interregional Crime and Daniel Grushkin Justice Research Institute GenSpace Simon Mercer, Ph.D. Kenneth Oye, Ph.D. Microsoft Research Massachusetts Institute of Technology Piers Millet, Ph.D. David Shepherd, M.A., M.S. International Fellow at Woodrow Wilson U.S. Department of Homeland Security Center Gilbert S. Omenn, M.D., Ph.D. University of Michigan 3 | P a g e AAAS Center for Science, Jen January Therrien Technology, and Security Policy Staff Visiting Scholar Kavita M. Berger, Ph.D. Zachary Watterson Associate Director Former Fellow Jennifer Roderick, M.Sc., M.S. Program Associate FBI WMD Directorate Nicholas Bashour Biocountermeasures Unit Staff Former intern Edward You, M.S. Donghun Kang Supervisory Special Agent Asan Academy Fellow William K. So, Ph.D. Bitna Lee Policy and Program Specialist Asan Academy Fellow Sonia Hunt, Ph.D. Eunhye Kim Management and Program Analyst Asan Academy Fellow 4 | P a g e Table of Contents Report in Brief: National and Transnational Security Implications of Big Data in the Life Sciences ...........................................................................................................................................7 What is Big Data? ........................................................................................................................8 Figure A: Technical Challenges and Current Solutions of Big Data in the Life Sciences .......9 Benefits, Risks, and Solutions for Big Data in the Life Sciences ................................................9 Risk and Benefit Assessment Frameworks ............................................................................11 Legal, Technical, Institutional, and Individual Solutions ......................................................11 Recommendations and Conclusions ..........................................................................................11 Chapter 1: National Security and Big Data in the Life Sciences .............................................13 Special Considerations to Big Data in the Life Sciences ...........................................................17 Project .........................................................................................................................................17 Chapter 2: Big Data in the Life Sciences ...................................................................................19 What is Big Data? ......................................................................................................................21 Figure 2.1 Data Analysis Technologies .................................................................................22 Understanding Big Data in the Life Sciences ........................................................................23 Investments in Big Data in the Life Sciences ............................................................................23 Strategic Drivers .........................................................................................................................25 Case Study: Omics and Drug Development ...........................................................................27 Case Study: Healthcare Delivery and “Learning Healthcare Systems” .................................28 Case Study: Ecosystems and the Changing Climate ..............................................................29 Challenges in Applying Big Data Technologies ........................................................................31 Conclusions ................................................................................................................................32 Chapter 3: Assessing National Security Risks and Benefits of Big Data in the Life Sciences ........................................................................................................................................................34 Dealing with Data: The “Dual Use Dilemma” Redefined .........................................................35 Risk and Benefit Scenarios ........................................................................................................36 Scenario 1: Targeting of Subpopulations using Big Data ......................................................37 Scenario 2: Misdirection ........................................................................................................40 5 | P a g e Scenario 3: Avoiding Detection .............................................................................................42 Assessment Frameworks ............................................................................................................44 Risk Assessment Framework .................................................................................................44 Figure 3.1: Conceptual Framework for Qualitative Risk Assessment for Emerging and Enabling Technologies.......................................................................................................46 Risk Assessment Framework Questions ............................................................................47 Benefit Assessment Framework .............................................................................................48 Figure 3.2: Benefit Assessment Framework for Big Data in the Life Sciences Technologies ......................................................................................................................49
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