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    Artificial Intelligence and the Right to Communicate to the Public in Copyright: Lessons from the United States

    Access Status
    Open access via publisher
    Authors
    Foong, Cheryl
    Date
    2022
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Foong, C. 2022. Artificial Intelligence and the Right to Communicate to the Public in Copyright: Lessons from the United States. Australian Intellectual Property Journal. (33): pp. 73-73.
    Source Title
    Australian Intellectual Property Journal
    Additional URLs
    https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4259598
    Faculty
    Faculty of Business and Law
    School
    Curtin Law School
    URI
    http://hdl.handle.net/20.500.11937/91744
    Collection
    • Curtin Research Publications
    Abstract

    Abstract Automated systems increasingly determine what copyright consumers encounter on online platforms, by drawing insights from vast sources of personal data. This article considers how artificial intelligence (AI) could play a part in the dissemination of content and the copyright infringement rules or principles that should apply. If an AI system selects copyright content and that content is automatically communicated to the public without authorisation, should the platform proprietor be liable for the resulting infringement and on what basis? The article analyses US and Australian copyright law on the right to communicate to the public and contrasts strict liability principles (such as volition) with fault-based secondary liability principles. Importantly, it analyses the policy considerations underlying these legal principles and critically evaluates the suitability of current laws to address automated communications. This article explains how Australian law could draw from United States vicarious liability principles to address AI-facilitated communications, as such principles could bridge the gap between strict and fault liability. The principles provide a more nuanced approach that permits consideration of the characteristics of AI systems on a case-by-case basis, rather than casting a wide net that extends primary infringement to all choices delegated to AI systems.

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