Artificial Intelligence Affordances: Deep-fakes as Exemplars of AI Challenges to Criminal Justice Systems

Research output: Contribution to journalJournal articleResearchpeer-review

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Artificial Intelligence Affordances: Deep-fakes as Exemplars of AI Challenges to Criminal Justice Systems. / Liu, Hin-Yan; Mazibrada, Andrew.

In: UNICRI Special Collection on Artificial Intelligence, 08.2020, p. 59-70.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Liu, H-Y & Mazibrada, A 2020, 'Artificial Intelligence Affordances: Deep-fakes as Exemplars of AI Challenges to Criminal Justice Systems', UNICRI Special Collection on Artificial Intelligence, pp. 59-70.

APA

Liu, H-Y., & Mazibrada, A. (2020). Artificial Intelligence Affordances: Deep-fakes as Exemplars of AI Challenges to Criminal Justice Systems. UNICRI Special Collection on Artificial Intelligence, 59-70.

Vancouver

Liu H-Y, Mazibrada A. Artificial Intelligence Affordances: Deep-fakes as Exemplars of AI Challenges to Criminal Justice Systems. UNICRI Special Collection on Artificial Intelligence. 2020 Aug;59-70.

Author

Liu, Hin-Yan ; Mazibrada, Andrew. / Artificial Intelligence Affordances: Deep-fakes as Exemplars of AI Challenges to Criminal Justice Systems. In: UNICRI Special Collection on Artificial Intelligence. 2020 ; pp. 59-70.

Bibtex

@article{9eb0f2cb985f4fcd81b5312c9dd12cd0,
title = "Artificial Intelligence Affordances: Deep-fakes as Exemplars of AI Challenges to Criminal Justice Systems",
abstract = "This paper attempts to provide a unifying conceptual framework to interpret the perils and promises of artificial intelligence (AI) applications in criminal justice systems. As platform technologies that support a myriad of potential applications, the impact of AI systems upon criminal justice will be amplificatory, divergent, and simultaneous. A unifying framework will facilitate a holistic appreciation of why AI systems might destabilise criminal justice systems and suggest appropriate responses depending on the type of criminal legal disruption at its root. We elaborate this theoretical framework by analysing problems posed by generative “deep-fake” technology, in the context of the criminal justice system in England and Wales. The value of this framework is that it forces us to ask questions about pre-existing normative and procedural responses in a way that reveals future problems, instead of bounded discussions of the application to and adaptability of existing systems which does not.",
author = "Hin-Yan Liu and Andrew Mazibrada",
year = "2020",
month = "8",
language = "English",
pages = "59--70",
journal = "UNICRI Special Collection on Artificial Intelligence",

}

RIS

TY - JOUR

T1 - Artificial Intelligence Affordances: Deep-fakes as Exemplars of AI Challenges to Criminal Justice Systems

AU - Liu, Hin-Yan

AU - Mazibrada, Andrew

PY - 2020/8

Y1 - 2020/8

N2 - This paper attempts to provide a unifying conceptual framework to interpret the perils and promises of artificial intelligence (AI) applications in criminal justice systems. As platform technologies that support a myriad of potential applications, the impact of AI systems upon criminal justice will be amplificatory, divergent, and simultaneous. A unifying framework will facilitate a holistic appreciation of why AI systems might destabilise criminal justice systems and suggest appropriate responses depending on the type of criminal legal disruption at its root. We elaborate this theoretical framework by analysing problems posed by generative “deep-fake” technology, in the context of the criminal justice system in England and Wales. The value of this framework is that it forces us to ask questions about pre-existing normative and procedural responses in a way that reveals future problems, instead of bounded discussions of the application to and adaptability of existing systems which does not.

AB - This paper attempts to provide a unifying conceptual framework to interpret the perils and promises of artificial intelligence (AI) applications in criminal justice systems. As platform technologies that support a myriad of potential applications, the impact of AI systems upon criminal justice will be amplificatory, divergent, and simultaneous. A unifying framework will facilitate a holistic appreciation of why AI systems might destabilise criminal justice systems and suggest appropriate responses depending on the type of criminal legal disruption at its root. We elaborate this theoretical framework by analysing problems posed by generative “deep-fake” technology, in the context of the criminal justice system in England and Wales. The value of this framework is that it forces us to ask questions about pre-existing normative and procedural responses in a way that reveals future problems, instead of bounded discussions of the application to and adaptability of existing systems which does not.

M3 - Journal article

SP - 59

EP - 70

JO - UNICRI Special Collection on Artificial Intelligence

JF - UNICRI Special Collection on Artificial Intelligence

ER -

ID: 243910795