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Artificial Intelligence, Autonomous Drones and Legal Uncertainties

Published online by Cambridge University Press:  03 August 2022

Jacques Hartmann*
Affiliation:
University of Dundee, School of Law, Dundee, UK
Eva Jueptner
Affiliation:
University of Dundee, School of Law, Dundee, UK
Santiago Matalonga
Affiliation:
University of the West of Scotland, Glasgow, UK
James Riordan
Affiliation:
University of the West of Scotland, Glasgow, UK
Samuel White
Affiliation:
University of the West of Scotland, Glasgow, UK
*
*Corresponding author. Email: jhartmann@dundee.ac.uk
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Abstract

Drones represent a rapidly developing industry. Devices initially designed for military purposes have evolved into a new area with a plethora of commercial applications. One of the biggest hindrances in the commercial developments of drones is legal uncertainty concerning the legal regimes applicable to the multitude of issues that arises with this new technology. This is especially prevalent in situations concerning autonomous drones (ie drones operating without a pilot). This article provides an overview of some of these uncertainties. A scenario based on the fictitious but plausible event of an autonomous drone falling from the sky and injuring people on the ground is analysed from the perspectives of both German and English private law. This working scenario is used to illustrate the problem of legal uncertainty facing developers, and the article provides valuable knowledge by mapping real uncertainties that impede the development of autonomous drone technology alongside providing multidisciplinary insights from law as well as software electronic and computer engineering.

Information

Type
Articles
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press
Figure 0

Figure 1 A simple artificial neural network and a deep learning neural network.