Artificial intelligence, robotics, ethics, and the military: a Canadian perspective
Open accessAbstract
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 measuredAI 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)
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Cites
1,500 scientists lift the lid on reproducibility
Baker, M. (2016) · Nature · doi:10.1038/533452a · Cited by 4,209 · see citing works
Programmable self-assembly in a thousand-robot swarm
Rubenstein, M., Cornejo, A., & Nagpal, R. (2014) · Science · doi:10.1126/science.1254295 · Cited by 1,266 · see citing works
Artificial neural networks in medical diagnosis
Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013) · Journal of Applied Biomedicine · doi:10.2478/v10136-012-0031-x · Cited by 898 · see citing works
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1802.07228 · Cited by 849 · see citing works
Flagged first, then by citations
Research works printed but not linked: 22
- 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
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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
- 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/.
- 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.
- 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.
- Hurst, J. 2017. Robotic Swarms in Offensive Maneuver. Joint Force Quarterly 87(4):105-11.
- Hutson, M. 2018. Artificial Intelligence Faces Reproducibility Crisis. Science 359(6377): 725-26. doi.org/10.1126/sci- ence.359.6377.725. · DOI
- 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
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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
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Artificial intelligence, machine learning and deep learning in advanced robotics, a review
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Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983 · see citing works
- 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
- hybrid
- doi.org
- 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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