Using 'big' metadata for criminal intelligence : understanding limitations and appropriate safeguards

Alana Maurushat; Lyria Bennett-Moses; David Vaile

Using 'big' metadata for criminal intelligence : understanding limitations and appropriate safeguards

Alana Maurushat, Lyria Bennett-Moses, David Vaile

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

7 Citations (Scopus)

Abstract

Using Internet Service Provider 'Big' metadata as a case study, we examine legal and ethical issues with machine learning Big Data tools developed and deployed in Australia for law enforcement intelligence purposes. In order to do this, we outline the benefits, limitations and risks of these tools, analyze current methods for de-identification and anonymisation, and discuss necessary safeguards.

Original language	English
Title of host publication	Proceedings of the 15th International Conference on Artificial Intelligence and Law (ICAIL 2015), University of San Diego School of Law, San Diego, 8 - 12 June, 2015
Publisher	ACM
Pages	196-200
Number of pages	5
ISBN (Print)	9781450335225
Publication status	Published - 2015
Event	International Conference on Artificial Intelligence and Law - Duration: 8 Jun 2015 → …

Conference

Conference	International Conference on Artificial Intelligence and Law
Period	8/06/15 → …

Keywords

big data
criminal investigation
law enforcement
machine learning
metadata

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inproceedings{14a8c5c4b3304aba93a6b28e222c1d35,

title = "Using 'big' metadata for criminal intelligence : understanding limitations and appropriate safeguards",

abstract = "Using Internet Service Provider 'Big' metadata as a case study, we examine legal and ethical issues with machine learning Big Data tools developed and deployed in Australia for law enforcement intelligence purposes. In order to do this, we outline the benefits, limitations and risks of these tools, analyze current methods for de-identification and anonymisation, and discuss necessary safeguards.",

keywords = "big data, criminal investigation, law enforcement, machine learning, metadata",

author = "Alana Maurushat and Lyria Bennett-Moses and David Vaile",

year = "2015",

language = "English",

isbn = "9781450335225",

pages = "196--200",

booktitle = "Proceedings of the 15th International Conference on Artificial Intelligence and Law (ICAIL 2015), University of San Diego School of Law, San Diego, 8 - 12 June, 2015",

publisher = "ACM",

note = "International Conference on Artificial Intelligence and Law ; Conference date: 08-06-2015",

}

Maurushat, A, Bennett-Moses, L & Vaile, D 2015, Using 'big' metadata for criminal intelligence : understanding limitations and appropriate safeguards. in Proceedings of the 15th International Conference on Artificial Intelligence and Law (ICAIL 2015), University of San Diego School of Law, San Diego, 8 - 12 June, 2015. ACM, pp. 196-200, International Conference on Artificial Intelligence and Law, 8/06/15.

Using 'big' metadata for criminal intelligence : understanding limitations and appropriate safeguards. / Maurushat, Alana; Bennett-Moses, Lyria; Vaile, David.
Proceedings of the 15th International Conference on Artificial Intelligence and Law (ICAIL 2015), University of San Diego School of Law, San Diego, 8 - 12 June, 2015. ACM, 2015. p. 196-200.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Using 'big' metadata for criminal intelligence : understanding limitations and appropriate safeguards

AU - Maurushat, Alana

AU - Bennett-Moses, Lyria

AU - Vaile, David

PY - 2015

Y1 - 2015

N2 - Using Internet Service Provider 'Big' metadata as a case study, we examine legal and ethical issues with machine learning Big Data tools developed and deployed in Australia for law enforcement intelligence purposes. In order to do this, we outline the benefits, limitations and risks of these tools, analyze current methods for de-identification and anonymisation, and discuss necessary safeguards.

AB - Using Internet Service Provider 'Big' metadata as a case study, we examine legal and ethical issues with machine learning Big Data tools developed and deployed in Australia for law enforcement intelligence purposes. In order to do this, we outline the benefits, limitations and risks of these tools, analyze current methods for de-identification and anonymisation, and discuss necessary safeguards.

KW - big data

KW - criminal investigation

KW - law enforcement

KW - machine learning

KW - metadata

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:48353

M3 - Conference Paper

SN - 9781450335225

SP - 196

EP - 200

BT - Proceedings of the 15th International Conference on Artificial Intelligence and Law (ICAIL 2015), University of San Diego School of Law, San Diego, 8 - 12 June, 2015

PB - ACM

T2 - International Conference on Artificial Intelligence and Law

Y2 - 8 June 2015

ER -

Using 'big' metadata for criminal intelligence : understanding limitations and appropriate safeguards

Abstract

Conference

Keywords

UN SDGs

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