Game Changers Artificial Intelligence Part III House Oversight Committee Subcommittee on IT Prepared Testimony and Statement for the Record of Ben Buchanan Postdoctoral Fellow Belfer Center Cybersecurity Project Harvard University Thank you Chairman Hurd and Ranking Member Kelly for holding this important hearing and for inviting me to testify My name is Ben Buchanan I am a fellow at Harvard University’s Belfer Center for Science and International Affairs and a Global Fellow at the Woodrow Wilson International Center for Scholars My research specialty is examining how nations deploy technology and I especially focus on cybersecurity and artificial intelligence Recently with Taylor Miller of the Icahn School of Medicine at Mount Sinai I co-authored a paper entitled “Machine Learning for Policymakers ” 1 To help open today’s hearing on artificial intelligence I’d like to make three points one on privacy one on cybersecurity and one on economic impact First to simplify a bit we can think of most modern artificial intelligence systems as relying on a triad of pillars data computing power and learning algorithms While we have seen remarkable advances in computer hardware and machine learning software for many policy purposes it is the role of data that is most vital to understand Data is the fuel of machine learning systems without it these systems produce embarrassingly poor results Gathering relevant and representative data for training development and testing purposes is a key part of building modern artificial intelligence technology On balance the more data that is fed into a machine learning system the more effective it will be It is no exaggeration to say that there are probably many economic scientific and technological breakthroughs that have not yet occurred because the right data sets have not yet been assembled 2 However there is a catch and a substantial one much of the data that might—and I emphasize might—be useful for future machine learning systems is intensely personal revealing and appropriately private Too frequently the allure of gathering more data to feed a machine learning system distracts from the harms that collecting that data brings There is the risk of breaches by hackers of misuse by those who collect it or access it and of secondary use—in which data collected for one purpose is later re-appropriated for another Frequently attempts at anonymization do not work nearly as well as promised It suffices to say that in my view any company or government agency collecting large amounts of data is assuming an enormous 1 Buchanan Ben and Taylor Miller “Machine Learning for Policymakers ” Belfer Center for Science and International Affairs 2017 https www belfercenter org sites default files files publication MachineLearningforPolicymakers pdf 2 Michele Banko and Eric Brill 'Scaling to Very Very Large Corpora for Natural Language Disambiguation' paper presented at 'Proceedings of the 39th Annual Meeting on Association for Computational Linguistics' 2001 responsibility Too often these collectors fall far short of meeting that responsibility And yet in an era of increased artificial intelligence the incentive to collect ever more data is only going to grow Technology cannot replace policy but some important innovations offer some mitigation to this problem Technical approaches such as differential privacy ensure that the particular data of any one individual is obscured but that large data sets retain almost all of their value On-device processing reduces the amount of data transmitted back to central servers and makes interception and aggregation of private information harder But these technological advances are too infrequently deployed especially when they conflict with short-term financial interests This is an area in which much remains to be done Second AI is poised to make a significant impact in cybersecurity potentially redefining key parts of the industry Automation on offense and defense is an area of enormous significance The most high-profile example of this is the DARPA Grand Cyber Challenge performed live at the DEF CON hacking conference in 2016 in which automated computer systems played both offense and defense against one another in a hacking competition In the long run it is uncertain whether increased automation will give a decisive cybersecurity advantage to hackers or to defenders but there is no doubt of its immediate relevance 3 AI systems also pose new kinds of cybersecurity challenges Most significant among these is the field known as adversarial learning in which the learning mechanisms of algorithms can be misled This can cause AI systems to make bizarre and unpredictable decisions The more powerful the system the potent such a mistake can be Cybersecurity is challenging enough to begin with adding in modern AI technology such as deep learning only makes it more so Research in the world of AI system security is fairly early-stage especially compared to the much more developed body of mainline cybersecurity research and best practices A more general security concern is AI safety AI safety is the field of research and development that ensures that AI systems once deployed remained aligned with the original interests of their designers and do not pose unanticipated threats This is not a question of Terminator scenarios Rather it is usually far subtler but vitally important and too frequently neglected I think it is fair to say we have barely scratched the surface of the important safety and basic security research that can be done in AI and that the United States should be a leader in these areas 4 Third AI will have significant economic effects Some of these to be sure will be positive On the other hand some will be quite negative The drumbeat of beneficial technological progress has always brought some upheaval with it Nonetheless my concern in this instance is that the further development and integration of AI will occur at such a rapid rate that its economic impacts might be hard to anticipate and counteract where appropriate Some research already clearly supports this view one major Oxford study cited in a landmark White House report suggests that 47% of jobs 3 Schneier Bruce “Artificial Intelligence and the Attack Defense Balance ” IEEE Security and Privacy 2018 https www schneier com essays archives 2018 03 artificial_intellige html 4 For one of the leading works in this area see Amodei Dario et al “Concrete problems in AI safety ” arXiv preprint 1606 06565 2016 For further development of these ideas and others see “Example Topics AI Alignment ” Open Philanthropy 2017 https www openphilanthropy org focus globalcatastrophic-risks potential-risks-advanced-artificial-intelligence open-philanthropy-project-ai-fellowsprogram#examples could be threatened by machines while another McKinsey analysis places the number at 30% Other studies bear out a worrying trend even when economic theory predict jobs lost to automation would be replaced later empirical analysis suggests that this replacement did not occur in practice 5 6 We must make sure that our current and future workforce is competitive in an age in which AI will be ubiquitous when it is as broadly integrated throughout our society as electricity according to Stanford professor and leading AI researcher Andrew Ng We should recognize that many American students and workers will need to have an understanding of computer science statistics and machine learning in order to be most competitive in the global economy too often these subjects are not taught in our schools or not taught well and with appropriate resources Innovations like Massive Open Online Courses or MOOCs have been immensely important in helping Americans acquire these valuable skills but government and formal educational settings have important roles to play as well Other nations such as China have begun to dramatically invest in these areas of education and research and we must do the same Simply put AI is exciting economically vital and geopolitically important Maximizing its potential will require a whole of society effort I appreciate your efforts in holding this series of hearings and I look forward to your questions 5 Frey Carl and Michael Osborne “The Future of Employment How Susceptible Are Jobs to Computerization ” Oxford University 2013 http www oxfordmartin ox ac uk downloads academic The_Future_of_Employment pdf See also Manyika James et al “Jobs Lost Jobs Gained Workforce Transitions in a Time of Automation ” McKinsey Global Institute 2017 https www mckinsey com media McKinsey Global%20Themes Future%20of%20Organizations What %20the%20future%20of%20work%20will%20mean%20for%20jobs%20skills%20and%20wages MGI-JobsLost-Jobs-Gained-Report-December-6-2017 ashx 6 Acemoglu Daron and Pascual Restrepo “The Race between Machine and Man Implications of Technology for Growth Factor Shares and Employment” NBER 2016 https www nber org papers w22252 pdf Acemoglu Daron and Pascual Restrepo “‘Robots and Jobs Evidence from US Labor Markets ” NBER 2017 https www nber org papers w23285 Committee on Oversight and Government Reform Witness Disclosure Requirement — “Truth in Testimony” Pursuant to House Rule XI clause 2 g 5 and Committee Rule 16 a non-governmental witnesses are required to provide the Committee with the information requested below in advance of testifying before the Committee You may attach additional sheets if you need more space Name Ben Buchanan 1 Please list any entity you are representing in Name of Entity r testi n e re t e ittee and briefly describe your relationship with e Your relationship with the entity entit None 2 Please list any federal grants or contracts including subgrants or subcontracts you or the entity or entities listed above have received since January 1 2015 that are related to the subject of the hearing Recipient of the grant or Grant or Contract Agency Program Source Amount contact you or entity above Name None Please list any payments or contracts including subcontracts you or the entity or entities listed above have received since January 1 2015 from a foreign government that are related to the subject of the hearing Recipient of the grant or Grant or Contract Agency Program Source Amount contact you or entity above Name None I certify that the information above and attached is true and correct to the best of my knowledge Signature ___________________________________________ April 6 2018 Date ______________ 1 1 Page ____ of ____ Ben Buchanan is a Postdoctoral Fellow at Harvard University’s Cybersecurity Project at the Belfer Center for Science and International Affairs where he conducts research on the intersection of cybersecurity artificial intelligence and statecraft His first book The Cybersecurity Dilemma was published by Oxford University Press in 2017 Previously he has written journal articles and peer-reviewed papers on artificial intelligence attributing cyber attacks deterrence in cyber operations cryptography election cybersecurity and the spread of malicious code between nations and non-state actors He is also a regular contributor to War on the Rocks and Lawfare and has published op-eds in the Washington Post and other outlets Ben received his PhD in War Studies from King’s College London where he was a Marshall Scholar and earned masters and undergraduate degrees from Georgetown University