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10 J. Nat'l Sec. L. & Pol'y 1 (2019-2020)
Machine Learning, Artificial Intelligence, and the Use of Force by States

handle is hein.journals/jnatselp10 and id is 6 raw text is: ARTICLES Machine Learning, Artificial Intelligence, and the Use of Force by States Ashley Deeks*, Noam Lubell**, & Daragh Murray*** [Olne bad algorithm and you're at war.T INTRODUCTION Big data technology and machine learning techniques play a growing role across all areas of modern society. Machine learning provides the ability to pre- dict likely future outcomes, to calculate risks between competing choices, to make sense of vast amounts of data at speed, and to draw insights from data that would be otherwise invisible to human analysts.1 As such, the use of machine learning presents a significant opportunity to transform how we understand and engage with issues across a range of subject areas, and to use this more-developed understanding to enhance decision-making. Although still at a relatively early stage, the deployment of machine learning algorithms has already begun. For example, doctors use machine learning algorithms to match symptoms to a partic- ular illness, or to identify individuals at risk of developing a particular health con- dition to target preventive intervention.2 In a criminal justice context, courts use machine learning algorithms to establish individual risk profiles and to predict the likelihood that a particular individual will re-offend. The private sector uses similar techniques for tasks as diverse as determining credit ratings and targeting advertising.4 * University of Virginia Law School. © 2019, Ashley Deeks, Noam Lubell & Daragh Murray. ** University of Essex. *** University of Essex. This work was supported by the Economic and Social Research Council grant number ES/M010236/1. t Jenna McLaughlin, Artificial Intelligence Will Put Spies Out of Work, FOREIGN POL'Y (June 9, 2017, 2:37 PM), https://foreignpolicy.com/2017/06/09/artificial-intelligence-will-put-spies-out-of- work-too/ (quoting Justin Cleveland). 1. LEE RAINIE & JANNA ANDERSON, PEW RES. CTR., CODE-DEPENDENT: PROS AND CONS OF THE ALGORITHM AGE 2-41 (Feb. 8, 2017), http://assets.pewresearch.org/wp-content/uploads/sites/14/2017/ 02/08181534/PI_2017.02.08_Algorithms FINAL.pdf. 2. See, e.g., Daniel Faggella, 7 Applications of Machine Learning in Pharma and Medicine, TECH EMERGENCE (July 19, 2018), https://www.techemergence.com/machine-learning-in-pharma-medicine/. 3. See State v. Loomis, 881 N.W.2d 749 (Wis. 2016). 4. See, e.g., Bill Hardekopf, Your Social Media Posts May Soon Affect Your Credit Score, FORBES (Oct. 23, 2015, 5:04 PM), https://www.forbes.com/sites/moneybuilder/2015/10/23/your-social-media- posts-may-soon-affect-your-credit-score-2/#2fOl53fOe4ed; Aaron Rieke, Google Was Right to Get Tough on Payday Loan Ads - and Now, Others Should Follow Suit, MEDIUM (May 13, 2016), https:// medium.com/equal-future/google -was -right-to- get-tough-on-payday- loan-ads -and-now-others -should- follow- suit-c7dd8446dc9 1.

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