The (Non) Deus-Ex Machina: A Realistic Assessment of Machine Learning for Countering Domestic Terrorism

Abstract

In light of the January 6 insurrection, the Department of Homeland Security (DHS) and other national security agencies are looking toward using more artificial intelligence (AI) and machine learning (ML) tools to detect and combat extremism in America. AI and ML hold much promise for the domestic CT mission, but the discourse has placed on them unrealistic expectations that do not conform to what is technically possible. This essay seeks to create a baseline conversation about what is ML, how it actually works, and what is a more realistic use case for ML in domestic CT. The core argument is that current ML tools are not optimal for the CT enterprise because terrorism experts are often sidelined in the development and the implementation of these algorithms.

Acknowledgments

The author of this paper would like to give a special thanks to Gary Shiffman and Harsh Pandya. Both played critical roles in the genesis of this project through their perspicacious critiques of the state-of-the-art of machine learning as it is applied to the counterterrorism enterprise. Their perspectives prompted the initial investigation into the disconnect between the promise of ML and its discomfiting reality. The author would also like to express his gratitude to Patricia Cogswell, Brian Drake, and Shane Quinlan for carving time out of their busy schedules to converse at length about the policy and technical aspects of machine learning for counterterrorism. They read early drafts, provided important feedback, and gave the proper policy context that illuminated the challenges of data and CT. Lastly, the author would like to thank Dylan Marshall and Alexander Meleagrou-Hitchens who served as sounding boards for many of the ideas contained within the while they were in an inchoate state. Machine-learning, in spite the simplicity implied by its name, can lead to analytical labyrinths, and those who supported this project were the proverbial torch bearers that prevented me from getting lost.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

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9 Brian Drake in discussion with the author, July 2021.

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12 Ken Dilanian and Julia Ainsley, “DHS Weighing Major Changes to Fight Domestic Violent Extremism, Say Officials,” NBC News, 25 March 2021, https://www.nbcnews.com/politics/national-security/dhs-weighing-huge-changes-fight-domestic-violent-extremism-say-officials-n1262047

13 Ibid.

14 Ibid.

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32 Ganor, “Artificial or Human,”

33 McKendrick, Artificial Intelligence Prediction; John Harper, “Artificial Intelligence Could Help Neutralize Enemy Bombs,” National Defense Magazine, 18 September 2017, https://www.nationaldefensemagazine.org/articles/2017/9/18/artificial-intelligence-could-help-neutralize-enemy-bombs

34 Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis, “Explianable AI: A Review of Machine Learning Interpretability Methods,” Entropy 23, no. 18 (2020).

35 Patricia Cogswell in discussion with the author, July 2021.

36 Ibid.

37 Eda Kavlakoglu, “AI vs. Machine Learning vs. Deep Learning vs. Neural Networks; What’s the Difference,” IBM, 27 May 2020, https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks

38 Martin van Creveld, The Rise and Decline of the State (Cambridge, UK: Cambridge University Press, 1999), 145–7.

39 Judea Pearl, “Causal Inference in Statistics: An Overview,” Statistics Survey 3 (2009), 99.

40 Ibid.

41 Eli Berman, Jacob N. Shapiro, and Joseph H. Felter, “Can Hearts and Minds Be Bought? The Economics of Counterinsurgency in Iraq,” Journal of Political Economy 119, no. 4 (2011); Jacob N. Shapiro and Nils B. Weidmann, “Is the Phone Mightier Than the Sword? Cellphones and Insurgent Violence in Iraq,” International Organization 69, no. 2 (2015).

42 Daniel Kahneman, Thinking Fast and Slow (New York: Farrar, Straus and Giroux, 2011), 3–12.

43 David Gunning and David W. Aha, “DARPA’s Explainable Artificial Intelligence Program,” AI Magazine 40, no. 2 (2019): 45.

44 Manuel DeLanda, War in the Age of Intelligent Machines (New York: Zone Books, 1991).

45 Pat Langley, “The Changing science of machine learning,” Machine Learning 82 (2011): 275–9.

46 Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. (Boston, MA: Prentice Hall, 2010), 27.

47 Kosuke Imai, Quantitative Social Science: An Introduction (Princeton, NJ: Princeton University Press, 2017), 1–2.

48 Russell and Norvig, Artificial Intelligence, 27–8.

49 Murphy, Machine Learning, 2–16.

50 Ibid.

51 Ibid.

52 Ibid.

53 Justin Grimmer notes that most of those using big data in industry don’t have social scientific training. See Justin Grimmer, “We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together,” PS: Political Science & Politics 48, no. 1 (2014): 80.

54 Shane Quinlan in discussion with the author, August 2021.

55 Ibid.

56 Ibid.

57 Ibid.

58 Gary King, Robert O. Keohane, and Sidney Verba, Designing Social Inquiry: Scientific Inference in Qualitative Research (Princeton, NJ: Princeton University Press, 1994).

59 Ibid.

60 Kosuke Imai, Gary King, and Elizabeth Stuart, “Misunderstandings between Experimentalists and Observationalists about Causal Inference,” Journal of the Royal Statistical Society: Series A (Statistics in Society) 171, no. 2 (2008): 481–502.

61 Gary King, Robert O. Keohane, and Sidney Verba, “The Importance of Research Design in Political Science,” The American Political Science Review, 89, no. 2 (1995): 475.

62 Marc Mangel and Francisco J. Samaniego, “Abraham Wald’s Work on Aircraft Survivability,” Journal of the American Statistical Association 89, no. 386 (1984).

63 Ibid.

64 Robert A. Paper and Keven Ruby, “The Capitol Rioters Aren’t Like Other Extremists,” The Atlantic, 2 February 2021, https://www.theatlantic.com/ideas/archive/2021/02/the-capitol-rioters-arent-like-other-extremists/617895/

65 Jerusalem Demsas, “The Online Far Right is Angry, Exultant, and Ready for More,” Vox, 11 January 2021, https://www.vox.com/2021/1/9/22220716/antifa-capitol-storming-far-right-trump-biden-election-stop-the-steal-hawley-cruz

66 For an in-depth exploration see John Cheney-Lippold, We Are Data: Algorithms and the Making of Our Digital Selves (New York: New York University Press, 2017).

67 Kelsey Atheron, “When Big Data Went to War – and Lost,” Politico, 11 October 2017, https://www.politico.com/agenda/story/2017/10/11/counter-ied-warfare-data-project-000541/

68 Herbert A. Simon, “Spurious Correlation: A Causal Interpretation,” Journal of the American Statistical Associations 49, no. 267 (1954).

69 D.M.W. Powers, “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation,” Journal of Machine Learning Technologies 2, no. 1 (2011): 37–9.

70 Blakeley B. McShane, David Gal, Andrew Gelman, Christian Robert, and Jennifer L. Tackett, “Abandon Statistical Significance,” The American Statistician 73 (2019): 235–8.

71 Mick Ryan, “Intellectual Preparation for Future War: How Artificial Intelligence Will Change Professional Military Education,” War on the Rocks, 3 July 2018, https://warontherocks.com/2018/07/intellectual-preparation-for-future-war-how-artificial-intelligence-will-change-professional-military-education/

72 Jeffrey Dastin, “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women,” Reuters, 10 October 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G; Sidney Fussell, “Why Can’t This Soap Dispenser Identify Dark Skin?” Gizmodo, 17 August 2017, https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773

73 Gary J. Andres and Janice A. Beecher, “Applied Political Science: Bridging the Gap or a Bridge Too Far?” PS: Political Science and Politics 22, no. 3 (1989): 636–9.

74 Robert A. Pape, “The Strategic Logic of Suicide Terrorism,” American Political Science Review 97, no. 3 (2003).

75 Ibid.

76 Walter Laqueur and Christopher Wall, The Future of Terrorism (New York: Thomas Dunne Books, 2018), 11.

77 See Laura Rozen, “Researcher: Suicide terrorism linked to military occupation,” Politico, 11 October 2010, https://www.politico.com/blogs/laurarozen/1010/Researcher_Suicide_terrorism_linked_to_military_occupation.html

78 Scott Ashworth, Joshua D. Clinton, Adam Meirowitz, and Kristopher W. Ramsay, “Design, Inference, and the Strategic Logic of Suicide Terrorism,” American Political Science Review 49, no. 2 (2008).

79 David Gunning and David W. Aha, “DARPA’s Explainable Artificial Intelligence Program,” AI Magazine 40, no. 2 (2019).

80 Jeff Jonas and Jim Harper, Effective Counterterrorism and the Limited Role of Predictive Data Mining (Washington, DC: Cato, Policy Analysis no. 584, 2006), 6–10.

81 McKendrick, Artificial Intelligence, 9–10.

82 Irina Matijosaitiene, Anthony McDowald, and Vishal Juneja, “Predicting Safe Parking Spaces: A Machine Learning Approach to Geospatial Urban and Crime Data,” Sustainability 11, no. 10 (2019).

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84 Tarleton Gillepsie, “Content Moderation, AI, and the Question of Scale,” Big Data & Society (July–December 2020), 1–5.

85 Shane Quinlan in discussion with the author, August 2021.

86 Veheist, Stannat, and Mecacci, “Machine Learning against Terrorism,” 2978–9.

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91 On secure messaging platforms, see Brian Fishman, “Crossroads: Counter-Terrorism and the Internet,” Texas National Security Review 2, no. 2 (2019): 85–6.

92 Brian Drake in discussion with the author, July 2021.

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94 Brian Drake in discussion with the author, July 2021.

95 Dipayan Ghosh, “Are We Entering a New Era of Social Media Regulation?” Harvard Business Review, 14 January 2021,

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99 Jigsaw, Countermasures in Practice, Google, https://jigsaw.google.com/the-current/white-supremacy/countermeasures/

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102 Jeff Horwitz and Deepa Seetharaman, “Facebook Executives Shut Down Effots to Make the Site Less Divisive,” The Wall Street Journal, May 26, 2020, https://www.wsj.com/articles/facebook-knows-it-encourages-division-top-executives-nixed-solutions-11590507499

103 Amanda Lotz, “Profit, Not Free Speech, Governs Media Companies’ Decisions on Controversy,” The Conversation, 10 August 2018, https://theconversation.com/profit-not-free-speech-governs-media-companies-decisions-on-controversy-101292

104 Kashmir Hill, “The Secretive Company That Might End Privacy as We Know It,” The New York Times, 18 January 2020, https://www.nytimes.com/2020/01/18/technology/clearview-privacy-facial-recognition.html

105 Ibid.

106 A. Tarantola, “Why Clearview AI is a Threat to Us All,” Engadget, 12 February 2020, https://www.engadget.com/2020-02-12-clearview-ai-police-surveillance-explained.html

107 Ibid.

108 Thomas Brewster, “Hackers Use Little Stickers to Trick Tesla Autopilot Into the Wrong Lane,” Forbes, 1 April 2019, https://www.forbes.com/sites/thomasbrewster/2019/04/01/hackers-use-little-stickers-to-trick-tesla-autopilot-into-the-wrong-lane/?sh=6188f1a17c18

109 Brendan F. Klare, Mark J. Burge, Joshua C. Klontz, Richard W. Vorde Bruegge, and Anil K. Jain, “Face Recogniton Performance: Role of Demographic Information,” IEEE Transactions on Information Forensics and Security 7, no. 6 (2012): 1789–800.

110 Patrick Grother, Mei Ngan, Kayee Hanaoka, Face Recognition Vendor Test FRVT) Part 3: Demographic Effects, (Washington, DC: National Institute of Standards and Technology, December 2019), https://nvlpubs.nist.gov/nistpubs/ir/2019/nist.ir.8280.pdf

111 Ibid.

112 Ibid.

113 Alex Najibi, “Racial Discrimination in Face Recognition Technology,” Blog, Science Policy, Special Edition: Science Policy and Social Justice, Harvard University, 24 October 2020, https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/

114 Thomas J. Cowper and Michael E. Buerger, “Improving Our View of the World: Police and Augmented Reality Technology,” FBI Futures Working Group, 2011, https://www.fbi.gov/file-repository/stats-services-publications-police-augmented-reality-technology-pdf/view

115 Patricia Cogswell in discussion with the author, July 2021.

116 Ibid.

117 Russell Brandom, “How facial recognition helped police identify the Capital Gazette shooter,” The Verge, 29 June 2018, https://www.theverge.com/2018/6/29/17518364/facial-recognition-police-identify-capital-gazette-shooter

118 Harwell and Timberg, “How America’s surveillance”.

119 Homeland Security and Public Safety Division, Artificial Intelligence (AI) in Homeland Security.

120 Jose de Jesus Rocha-Salazar, Maria Jesuss Segovia Vargas, and Maria del Mar Camacho Minano, “Money Laundering and Terrorism Financing Detection Using Neural Networks and an Abnormality Indicator,” Expert Systems with Applications 169, no. 114470 (169), https://www.sciencedirect.com/science/article/pii/S0957417420311209

121 Gian Maria Campedelli, Iain Cruickshank, and Kathleen M. Carley, “A Complex Networks Approach to Find Latent Clusters of Terrorist Groups,” Applied Network Science 4, no. 1 (2019): 1–22.

122 Matthew Collin, “What the FinCEN Leaks Reveal about the Ongoing War on Dirty Money,” Brookings Institute, 25 September 2020, https://www.brookings.edu/blog/up-front/2020/09/25/what-the-fincen-leaks-reveal-about-the-ongoing-war-on-dirty-money/

123 Ibid.

124 Ibid; Richard K. Gordon, “Losing the War against Dirty Money: Rethinking Global Standards on Preventing Money Laundering and Terrorism Financing,” Duke Journal of Comparative & international Law 21, 2011.

125 FATF, Opportunities and Challenges of New Technologies for AML/CFT (Paris, France: FATF, July 2021), https://www.fatf-gafi.org/media/fatf/documents/reports/Opportunities-Challenges-of-New-Technologies-for-AML-CFT.pdf

126 Brian Drake in discussion with the author, July 2021.

127 Ibid.

128 Ibid.

129 Eric Halliday and Rachel Hanna, “How the Federal Government Investigates and Prosecutes Domestic Terrorism,” Lawfare, 16 February 2021, https://www.lawfareblog.com/how-federal-government-investigates-and-prosecutes-domestic-terrorism

130 Jigsaw, Countermeasures.

131 Mohammed M. Hafez, “The Ties That Bind: How Terrorists Exploit Family Bonds,” CTC Sentinel 9, no. 2 (2016), https://ctc.usma.edu/the-ties-that-bind-how-terrorists-exploit-family-bonds/

132 Patrcia Cogswell in discussion with the author, July 2021.

133 Shane Quinlan in discussion with the author, August 2021.

134 Ibid.

135 Ibid.

136 Jeff Schogol, “Why It’s So Difficult for the Military to Weed Out Extremists,” Task & Purpose, 19 February 2021, https://taskandpurpose.com/news/military-extremists-screening/

137 Hoffman, Inside Terrorism, 182–3.

138 Laura Hanu, James Thewlis, and Sasha Haco, “How AI Is Learning to Identify Toxic Online Content,” Scientific American, 8 February 2021, https://www.scientificamerican.com/article/can-ai-identify-toxic-online-content/

139 Sara Sidner and Mallory Simon, “How Robot, Explosives Took Out Dallas Sniper in Unprecedented Way,” CNN, 16 July 2016, https://www.cnn.com/2016/07/12/us/dallas-police-robot-c4-explosives

140 Sheena Chestnut Greitens, Myunghee Lee, and Emir Yazici, “Counterterrorism and Preventive Repression,” International Security 44, no. 3 (Winter 2019/2020): 9–47.

141 Ibid.