Towards a legal definition of machine intelligence: the argument for artificial personhood in the age of deep learning.

Karanasiou, A. P. & Pinotsis, D. A. (2017). Towards a legal definition of machine intelligence: the argument for artificial personhood in the age of deep learning. In: Jeroen Keppens & Guido Governatori (Eds.), ICAIL '17 Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law. (pp. 119-128). New York, NY: ACM. ISBN 978-1-4503-4891-1

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Abstract

The paper dissects the intricacies of Automated Decision Making (ADM) and urges for refining the current legal definition of AI when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. ADM relies upon a plethora of algorithmic approaches and has already found a wide range of applications in marketing automation, social networks, computational neuroscience, robotics, and other fields. Our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm; this can take various shapes and thus yield different answers to key issues regarding agency. The paper offers a fresh look at the concept of "Machine Intelligence", which exposes certain vulnerabilities in its current legal interpretation. Most importantly, it further helps us to explore whether the argument for "artificial personhood" holds any water. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of Human - Machine interaction and can thus serve as a point of reference for outlining distinct rights and obligations of the programmer and the consumer: driverless cars are used as a case study to explore the several layers of human and machine interaction. These different degrees of automation reflect various levels of complexities in the underlying algorithms, and pose very interesting questions in terms of agency and dynamic tasks carried out by software agents. Part 2 further discusses the intricate nature of the underlying algorithms and artificial neural networks (ANN) that implement them and considers how one can interpret and utilize observed patterns in acquired data. Is "artificial personhood" a sufficient legal response to highly sophisticated machine learning techniques employed in decision making that successfully emulate or even enhance human cognitive abilities?

Item Type: Conference or Workshop Item (Paper)
Additional Information: © Karanasiou, A. P. & Pinotsis, D. A. (2017). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ICAIL '17 Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law , http://dx.doi.org/10.1145/10.1145/3086512.3086524.
Uncontrolled Keywords: Machine Learning, ANN, Personhood hybrids, algorithmic agency
Divisions: School of Social Sciences > Department of Psychology
URI: http://openaccess.city.ac.uk/id/eprint/19422

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