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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: 


  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

  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

  * 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).
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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