Do androids dream of electric copyright? Comparative analysis of originality in artificial intelligence generated works

Guadamuz, Andres (2021) Do androids dream of electric copyright? Comparative analysis of originality in artificial intelligence generated works. In: Artificial Intelligence and Intellectual Property. Oxford University Press, pp. 147-176. ISBN 9780198870944

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Abstract

The advent of sophisticated artificial neural networks has opened new artistic opportunities, but also a variety of new legal challenges. Computer programs such as Google’s Deep Dream can take an image and process it in manners that resemble biological networks, producing artwork that is both unique and unpredictable. The law is not unfamiliar with the challenges of artificial intelligence (AI), in the past academics and policymakers have had to deal with the legal implications of autonomous agents in contract formation, to name just one area of interest. However, for the most part the implementation of smart systems has been limited in its reach and scope, and in many instances autonomous agents required quite a lot of direction from the programmer, following a very stringent set of rules. This meant that for the most part all rights, responsibilities, and liabilities arising from artificial agents fell squarely on the program creator. Neural networks are different, these systems have the potential to generate works in which human interaction is minimal. Modern copyright law has been drafted to consider originality as an embodiment of the author’s personality, and originality is one of the main requirements for the subsistence of copyright. So, what happens when you remove personality from the equation? Are machine-created works devoid of copyright? Do we need to change copyright law to accommodate autonomous artists? This chapter will explore this and other questions.

Item Type: Book Section
Schools and Departments: School of Law, Politics and Sociology > Law
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 10 Mar 2022 17:24
Last Modified: 10 Mar 2022 17:24
URI: http://sro.sussex.ac.uk/id/eprint/104804

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