Exploration of the Reliability of Criminal Sentencing: Assessing the impact of current sentencing systems in the USA on marginalised communities

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Exploration of the Reliability of Criminal Sentencing: Assessing the impact of current sentencing systems in the USA on marginalised communities

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Author(s): Aila Araghi

Publication date (Print): July 2024

Conference name: Proceedings of EVA London 2024 (EVA 2024)

Conference theme: Since 1990, the EVA London Conference has established itself as one of the United Kingdom’s most innovative and interdisciplinary conferences in the field of digital visualisation. The papers and abstracts in this volume cover areas such as the arts, culture, heritage, museums, music, performance, visual art, and visualisation, as well as related interdisciplinary areas, in combination with technology. The latest research and work by early career researchers, established scholars, practitioners, research students, and visual artists, can be found in this volume, published in full colour.

Conference date: 8–12 July 2024

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Aila Araghi

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Publication date: July 2024

Publication date (Print): July 2024

Pages: 353-355

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[0001]University of the Arts, London

Creative Computing Institute

45-65 Peckham Road, SE5 8UH, UK

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DOI: 10.14236/ewic/EVA2024.69

SO-VID: 552fafaf-0d8f-4790-b443-26e52529531c

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of EVA London 2024

Conference acronym: EVA 2024

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Conference location: London

Conference date: 8–12 July 2024

Conference sponsor: Electronic Workshops in Computing (eWiC)

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2024.69

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

Angwin, J., Larson, J., Mattu, S. and Kirchner, L. (2022) Machine bias. In Ethics of data and analytics (pp. 254-264). Auerbach Publications.
Barabas, C., Virza, M., Dinakar, K., Ito, J. and Zittrain, J. (2018) January. Interventions over predictions: Reframing the ethical debate for actuarial risk assessment. In Conference on fairness, accountability and transparency (pp. 62-76). PMLR.
Berk, R., (2010) Balancing the costs of forecasting errors in parole decisions. Alb. L. Rev., 74, p.1071.
Carse, J., (2011) Finite and infinite games. Simon and Schuster.
Danziger, S., Levav, J. and Avnaim-Pesso, L. (2011) Extraneous factors in judicial decisions. Proceedings of the National Academy of Sciences, 108(17), pp.6889-6892.
Freeman, K. (2016) Algorithmic injustice: How the Wisconsin Supreme Court failed to protect due process rights in State v. Loomis. North Carolina Journal of Law & Technology, 18(5), p.75.
Fry, H. (2018) Hello World: How to be Human in the Age of the Machine. Random House.
Hübner, D. (2021) Two kinds of discrimination in AI-based penal decision-making. ACM SIGKDD Explorations Newsletter, 23(1), pp.4-13.
Jones, D.M. (2015) A Bronx tale: Disposable people, the legacy of slavery, and the social death of Kalief Browder. U. Miami Race & Soc. Just. L. Rev., 6, p.31.
Kahneman, D. Sibony, O. and Sunstein, C.R., (2021) Noise: a flaw in human judgment. Hachette UK.
Suryadi, A. (2021) Responsiveness of Criminal Law to Skimming Crimes in The Era of Industrial Revolution 4.0 (Four Point Zero). Jurnal Hukum Volkgeist, 5(2), pp.130-142.
Vitiello, M. (2017) Brock Turner: Sorting through the noise. U. Pac. L. Rev., 49, p.631.
Wisser, L. (2019) Pandora's algorithmic black box: the challenges of using algorithmic risk assessments in sentencing. AM. crim. l. rev., 56, p.1811.

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[1] Angwin, J., Larson, J., Mattu, S. and Kirchner, L. (2022) Machine bias. In Ethics of data and analytics (pp. 254-264). Auerbach Publications.

[2] Barabas, C., Virza, M., Dinakar, K., Ito, J. and Zittrain, J. (2018) January. Interventions over predictions: Reframing the ethical debate for actuarial risk assessment. In Conference on fairness, accountability and transparency (pp. 62-76). PMLR.

[3] Berk, R., (2010) Balancing the costs of forecasting errors in parole decisions. Alb. L. Rev., 74, p.1071.

[4] Carse, J., (2011) Finite and infinite games. Simon and Schuster.

[5] Danziger, S., Levav, J. and Avnaim-Pesso, L. (2011) Extraneous factors in judicial decisions. Proceedings of the National Academy of Sciences, 108(17), pp.6889-6892.

[6] Freeman, K. (2016) Algorithmic injustice: How the Wisconsin Supreme Court failed to protect due process rights in State v. Loomis. North Carolina Journal of Law & Technology, 18(5), p.75.

[7] Fry, H. (2018) Hello World: How to be Human in the Age of the Machine. Random House.

[8] Hübner, D. (2021) Two kinds of discrimination in AI-based penal decision-making. ACM SIGKDD Explorations Newsletter, 23(1), pp.4-13.

[9] Jones, D.M. (2015) A Bronx tale: Disposable people, the legacy of slavery, and the social death of Kalief Browder. U. Miami Race & Soc. Just. L. Rev., 6, p.31.

[10] Kahneman, D. Sibony, O. and Sunstein, C.R., (2021) Noise: a flaw in human judgment. Hachette UK.

[11] Suryadi, A. (2021) Responsiveness of Criminal Law to Skimming Crimes in The Era of Industrial Revolution 4.0 (Four Point Zero). Jurnal Hukum Volkgeist, 5(2), pp.130-142.

[12] Vitiello, M. (2017) Brock Turner: Sorting through the noise. U. Pac. L. Rev., 49, p.631.

[13] Wisser, L. (2019) Pandora's algorithmic black box: the challenges of using algorithmic risk assessments in sentencing. AM. crim. l. rev., 56, p.1811.

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Exploration of the Reliability of Criminal Sentencing: Assessing the impact of current sentencing systems in the USA on marginalised communities

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