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51 Geo. J. Int'l L. 117 (2019-2020)
Machine Learning Weapons and International Humanitarian Law: Rethinking Meaningful Human Control

handle is hein.journals/geojintl51 and id is 120 raw text is: 






          MACHINE LEARNING WEAPONS AND
          INTERNATIONAL HUMANITARIAN LAW:
    RETHINKING MEANINGFUL HUMAN CONTROL


                             SHIN-SHIN HUA*

                                ABSTRACT

   AI's revolutionizing of warfare has been compared to the advent of the nu-
 clear bomb. Machine learning technology, in particular, is paving the way for
future automation of life-or-death decisions in armed conflict.
   But because these systems are constantly learning, it is difficult to predict
what  they will do or understand why they do it. Many therefore argue that they
should  be prohibited under international humanitarian law (IHL)  because
they cannot be subject to meaningful human control.
   But in a machine learning paradigm, human  control may become unneces-
sary or even detrimental to IHL compliance. In order to leverage the potential
of this technology to minimize casualties in conflict, an unthinking adherence
to the principle of the more control, the better should be abandoned.
   Instead, this Article seeks to define prophylactic measures that ensure
machine  learning weapons  can comply with IHL  rules. Further, it explains
how  the unique capabilities of machine learning weapons can facilitate a more
robust application of the fundamental IHL principle of military necessity.

  I.  INTRODUCTION   ....................................            118
  II. OVERVIEW  OF THE TECHNOLOGY   . . . . . . . . . . . . . . . . . . . . . . . . 121
      A.   Defining Autonomous Weapons  Systems ...............  .         121
           1.  Human-Machine Interactions ................ .         122
           2.  The  Task Performed   ....................... ..      122
      B.   M achine Learning.............................. ..        123
           1.  Deep  Learning............................ ..         124
           2.  Reinforcement   Learning  .................... .      125
           3.  Legally Relevant Attributes of Machine  Learning
               System s ..................................           126



  * Advanced Master of Laws (Leiden University, Netherlands), JD-equivalent (University of
Cambridge); Research Affiliate, Centre for the Study of Existential Risk, University of Cambridge;
Attorney, BT Group; former Attorney at Cleary Gottlieb Steen & Hamilton. With thanks to Horst
Fischer, Professor of International Humanitarian Law at Leiden University and Adjunct Professor
of International and Public Affairs at Columbia University, and Jacob Turner, barrister at
Fountain Court Chambers in London, for their valuable guidance and feedback. All opinions are
those of the author and do not reflect the views of any organization. © 2020, Shin-Shin Hua.


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