Cited by 42Ethics and Social Impacts of AIMilitary Strategy and TechnologyWar, Ethics, and Justification

Artificial intelligence, robotics, ethics, and the military: a Canadian perspective

Open access
Wasilow, Sherry, Thorpe, Joelle B.·2019·AI Magazine

Abstract

Defense and security organizations depend upon science and technology to meet operational needs, predict and counter threats, and meet increasingly complex demands of modern warfare. Artificial intelligence and robotics could provide solutions to a wide range of military gaps and deficiencies. At the same time, the unique and rapidly evolving nature of AI and robotics challenges existing polices, regulations, and values, and introduces complex ethical issues that might impede their development, evaluation, and use by the Canadian Armed Forces (CAF). Early consideration of potential ethical issues raised by military use of emerging AI and robotics technologies in development is critical to their effective implementation. This article presents an ethics assessment framework for emerging AI and robotics technologies. It is designed to help technology developers, policymakers, decision makers, and other stakeholders identify and broadly consider potential ethical issues that might arise with the military use and integration of emerging AI and robotics technologies of interest. We also provide a contextual environment for our framework, as well as an example of how our framework can be applied to a specific technology. Finally, we briefly identify and address several pervasive issues that arose during our research.

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  • Robotics is used in military operations for tasks such as reconnaissance, surveillance, and bomb disposal.

    “Robotics is used in military operations for tasks such as reconnaissance, surveillance, and bomb disposal.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [14]

    The paper’s swarming-technologies discussion says UAV swarms could be used for surveillance and reconnaissance. A note in “Summary and Future Considerations” mentions robotics and sensors for IED and explosive detection, but it does not establish bomb disposal; the swarm passage also describes a potential use rather than an established operation. (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • AI and ML algorithms are used in military robotics to analyze data and make decisions based on the information gathered.

    “AI and ML algorithms are used to analyze data and make decisions based on the information gathered [14] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [14]

    The Abstract says AI can analyze surveillance video and might combine sensor data to help military personnel make more informed decisions. It anticipates robots using machine-learning algorithms, but does not establish that AI and ML algorithms are already used in military robotics to make the decisions themselves. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 22

  1. Allen, G. and Chan, T. 2017. Artificial Intel- ligence and National Security. A US Intelli- gence Advanced Research Projects Activity Study. Cambridge, MA: Belfer Center for Sci- ence and International Affairs, Harvard
  2. Baillie, J. C. 2016. Why AlphaGo Is Not AI. IEEE Spectrum, March 17, 2016. spectrum. ieee.org/automaton/robotics/artificial-intel- ligence/why-alphago-is-not-ai.
  3. Banko, M., and Brill E. 2001. Scaling to Very Very Large Corpora for Natural Language Disambiguation. In Proceedings of the 39th Annual Meeting on Association for Computa- tional Linguistics, 26-33. San Francisco: Mor- gan Kaufmann.
  4. Booth, S.; Tompkin, J.; Pfister, H.; Waldo, J.; Gajos, K.; and Nagpal, R. 2017. Piggyback- ing Robots: Human-Robot Overtrust in Uni- versity Dormitory Security. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, 426-34. New York: Association for Computing Machin- ery. doi.org/10.1145/2909824.3020211. · DOI
  5. Buchanan, B., and Miller, T. 2017. Machine Learning for Policymakers: What It Is and Why It Matters. The Cyber Security Project. Cambridge, MA: Harvard Kennedy School, Belfer Center for Science and International Affairs.
  6. Carter, W. A.; Kinnucan, E.; and Elliot, J. 2018. A National Machine Intelligence Strategy for the United States: A Report of the CSIS Tech- nology Policy Program. Washington, DC: Cen- ter for Strategic and International Studies. Association for Computing Machinery, www.aies-conference.com.
  7. Couzin, I. D., and Krause, J. 2003. Self-Orga- nization and Collective Behavior in Verte- brates. In Advances in the Study of Animal Behavior, edited by P. Slater, J. Rosenblatt, C. Snowdon, and T. Roper, 1-75. Boston, MA: Academic Press.
  8. Deputy Assistant Secretary of the Army for Research and Technology (DASA R&T). 2017. Emerging Science and Technology Trends: 2017-2047. A Synthesis of Leading Forecasts. Unclassified Report. Providence, RI: FutureScout. IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. 2016. Ethically Aligned Design: A Vision for Prioritiz- ing Wellbeing with Artificial Intelligence and Autonomous Systems, Version 1. Piscataway, NJ: Institute for Electrical and Electronics Engineers. standards.ieee.org/develop/ind- conn/ec/autonomous_systems.html. IEEE Global Initiative on Ethics of Auton - omous and Intelligent Systems. 2017. A Vision for Prioritizing Human Well-Being with Autonomous and Intelligent Systems, Version 2. Piscataway, NJ: Institute for Electrical and Electronics Engineers. standards. ieee.org/ develop/indconn/ec/ead_ brochure _v2.pdf. European Parliament. 2016. European Civil Law Rules in Robotics. Study prepared for the European Parliament's Committee on Legal Affairs. Brussels, Belgium: Policy Depart- ment for Citizens' Rights and Constitution- al Affairs. www.europarl.europa.eu/RegDa- ta/etudes/STUD/2016/571379/IPOL_STU% 282016%29571379_EN.pdf.
  9. Floridi, L., and Taddeo, M., eds. 2014. The Ethics of Information Warfare. Law, Gover- nance and Technology 14. New York: Sprin - ger. doi.org/10.1007/978-3-319-04135-3. Future of Life Institute. 2017. Asilomar AI Principles. Winchester, MA: Future of Life Institute. futureoflife.org/ai-principles. · DOI
  10. Gallacher, J. D.; Barash, V.; Howard, P. N.; and Kelly, J. 2017. Junk News on Military Affairs and National Security: Social Media Disinformation Campaigns Against US Mili- tary Personnel and Veterans. Data Memo 2017.9. Oxford, UK: University of Oxford, Project on Computational Propaganda. comprop.oii.ox.ac.uk/research/working- papers/vetops/.
  11. Hawley, J. K. 2011. Not by Widgets Alone: The Human Challenge of Technology- intensive Military Systems. Army Forces Jour- nal, February 1, 2011. www.armedforces journal.com/not-by-widgets-al.
  12. Higgins, F.; Tomlinson, A.; and Martin, K. M. 2009. Threats to the Swarm: Security Considerations for Swarm Robotics. Inter- national Journal of Advances in Security 2(2- 3): 288-97.
  13. Hurst, J. 2017. Robotic Swarms in Offensive Maneuver. Joint Force Quarterly 87(4):105-11.
  14. Hutson, M. 2018. Artificial Intelligence Faces Reproducibility Crisis. Science 359(6377): 725-26. doi.org/10.1126/sci- ence.359.6377.725. · DOI
  15. Lazer, D. M. J.; Baum, M. A.; Benkler, Y.; Berinsky, A. J.; Greenhill, K. M.; Menczer, F.; Metzger, M. J.; Nyhan, B.; Pennycook, G.; Rothschild, D.; Schudson, M.; Sloman, S. A.; Sunstein, C. R.; Thorson, E. A.; Watts, D. J.; and Zittrain, J. L. 2018. The Science of Fake News. Science 359(6380): 1094-6.doi.org/10. 1126/science.aao2998. · DOI · Paper page
  16. Lin, P. 2016. Why Ethics Matters for Autonomous Cars. In Autonomous Driving: Technical, Legal and Social Aspects, edited by M. Maurer, J. C. Gerdes, B. Lenz, and H. Winner, 69-85. New York: Springer Nature. doi.org/10.1007/978-3-662-48847-8_4. · DOI
  17. Mlot, N. J.; Tovey, C. A.; and Hu, D. L. 2011. Fire Ants Self-Assemble into Waterproof Rafts to Survive Floods. Proceedings of the National Academy of Sciences of the United States 108(19): 7669-73. doi.org/10.1073/ pnas.1016658108. National Academy of Sciences. 2018. The Frontiers of Machine Learning: 2017 · DOI
  18. Osoba, O., and Welser, W. 2017. An Intelli- gence in Our Image: The Risk of Bias and Errors in Artificial Intelligence. Technical Report RR- 1744-RC. Santa Monica, CA: RAND Corpo- ration. doi.org/10.7249/RR1744. · DOI · Paper page
  19. Solis, G. D. 2016. The Law of Armed Conflict: International Humanitarian Law in War. Cam- bridge, UK: Cambridge University Press. doi.org/10.1017/CBO9781316471760. · DOI
  20. Stone, P.; Brooks, R.; Brynjolfsson, E.; Calo, R.; Etzioni, O.; Hager, G.; Hirschberg, J.; Kalyanakrishnan, S.; Kamar, E.; Kraus, S.; Leyton-Brown, K.; Parkes, D.; Press, W.; Saxenian, A.; Shah, J.; Tambe, M.; and Teller, A. 2016. 2016. Artificial Intelligence and Life in 2030: One Hundred Year Study on Artificial Intelligence. Report of the 2015- 2016 Study Panel. Stanford, CA: Stanford University. UK Atomic Energy Authority. 2016. Written Testimony Submitted by RACE, UK Atomic Energy Authority (ROB0041). In Robotics and Artificial Intelligence: Fifth Report of Ses- sion 2016-17. London, UK: UK Parliament, House of Commons, Science and Technolo- gy Committee. data.parliament.uk/ writ- tenevidence/committeeevidence.svc/evi- dencedocument/science-and-technology-c ommittee/robotics-and-artificial-intelli- gence/written/32640.htm.
  21. US Department of Defense. 2017. Depart- ment of Defense Announces Successful Micro-Drone Demonstration. Press Release NR-008-17. January 9, 2017. Washington, DC: US Department of Defense. www. defense.gov/News/News-Releases/News- Release-View/Article/1044811/department- of-defense-announces-successful-micro- drone-demonstration.
  22. Wright, D. 2011. A Framework for the Ethi- cal Impact Assessment of Information Tech- nology. Ethics and Information Technology 13(3): 199-226. doi.org/10.1145/968261. 968263. · DOI

Cited by

Sorted by citations

Authors
Wasilow, S., & Thorpe, J. B.
Affiliations
Defence Research and Development Canada (CA)
Venue
AI Magazine
Publisher
Wiley
Published
2019-03
Volume
40
Issue
1
Pages
37-48
ISSN
  • 0738-4602 (ISSN-L)
  • 2371-9621
OpenAlex
W2926068071
Cited by
42
References
4
Citations by year
2026: 2 · 2025: 5 · 2024: 13 · 2023: 10 · 2022: 2 · 2021: 3 · 2020: 4 · 2019: 3
FWCI
4.92
Type
journal article
Language
en
Open access
Locations
AI Magazine (publishedVersion, cc-by)
Funders
  • Government of Canada
  • Mitacs
  • Ministère de la Défense Nationale
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Engineering › Control and Systems Engineering › Military Strategy and Technology
  • Social Sciences › Arts and Humanities › Philosophy › War, Ethics, and Justification
Keywords
artificial intelligence in warfare, military robotics, Canadian Armed Forces, military ethics, defense policy, technology integration, autonomous weapons, ethics of emerging technologies

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