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Ethics of autonomous weapons systems and its applicability to any AI systems

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  • Gómez de Ágreda, Ángel

Abstract

Most artificial intelligence technologies are dual-use. They are incorporated into both peaceful civilian applications and military weapons systems. Most of the existing codes of conduct and ethical principles on artificial intelligence address the former while largely ignoring the latter. But when these technologies are used to power systems specifically designed to cause harm, the question must be asked as to whether the ethics applied to military autonomous systems should also be taken into account for all artificial intelligence technologies susceptible of being used for those purposes. However, while a freeze in investigations is neither possible nor desirable, neither is the maintenance of the current status quo. Comparison between general-purpose ethical codes and military ones concludes that most ethical principles apply to human use of artificial intelligence systems as long as two characteristics are met: that the way algorithms work is understood and that humans retain enough control. In this way, human agency is fully preserved and moral responsibility is retained independently of the potential dual-use of artificial intelligence technology.

Suggested Citation

  • Gómez de Ágreda, Ángel, 2020. "Ethics of autonomous weapons systems and its applicability to any AI systems," Telecommunications Policy, Elsevier, vol. 44(6).
  • Handle: RePEc:eee:telpol:v:44:y:2020:i:6:s0308596120300458
    DOI: 10.1016/j.telpol.2020.101953
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    References listed on IDEAS

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    1. Oecd, 2018. "AI: Intelligent machines, smart policies: Conference summary," OECD Digital Economy Papers 270, OECD Publishing.
    2. Straub, Jeremy, 2016. "Consideration of the use of autonomous, non-recallable unmanned vehicles and programs as a deterrent or threat by state actors and others," Technology in Society, Elsevier, vol. 44(C), pages 39-47.
    3. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
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