Algoritmos de valoración de riesgo Contexto, concepto y limitaciones
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Published:
27-11-2023
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Vol 44 No Ext. (2023): Inteligencia artificial y sistema penal, Articles, pages 1-29
Submitted: 30-11-2022
Accepted: 08-03-2023
Published: 27-11-2023
Abstract
Risk has always played a essential role in the criminal justice systems of the Global North. Its irruption, development and expansion is the result of a multi-causal process of enormous complexity which has always been part of Penal Law and Criminology.
Currently, issues such as Big Data, Machine Learning or new criminal policy paradigms seem to be drawing a scenario for which the criminal justice system is not quite ready. Thus, Algorithmic Risk Assessments (ARA) emerge as useful tools in order to offer potential solutions. ARAs are introduced as new instruments based on new methods and technologies capable to predict more and better than their predecessors.
Hence, this paper aims to review, from a critical perspective, how ARAs are able to enhance previous instruments performance and whether they are the best way to move towards a better criminal justice system.
Keywords:
Algoritmos de Valoración de Riesgo; Riesgo; Big Data; Machine Learning; PolíticaCriminal
Cited by
Article Details
References
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• GARB, H.N. / WOOD, J.M. (2019): “Methodological advances in statistical prediction”, en: Psychological Assessment, Vol. 31(12), pp.1456-1466.
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• HÄLTERLEIN, J. (2021): “Epistemologies of predictive policing: Mathematical social science, social physics and machine learning”, en: Big Data & Society, pp. 1-13.
• HANNAH-MOFFAT, K. (2019): “Algorithmic risk governance: Big Data analytics, race and information activism in criminal justice debates” en: Theoretical Criminology, Vol. 23(4), pp. 453-470.
• HANNAH-MOFFAT, K. / MAURUTTO, P. (2010): “Re-contextualizing pre-sentence reports. Risk and race”, en: Punishment and Society, Vol. 12(3), pp. 262-286.
• HARCOURT, B.E. (2003): “From the ne’er-do-well to the criminal history category: The refinement of the actuarial model in criminal law”, en: Law and Contemporary Problems, Vol. 66(3), pp. 99-151.
• HAYWARD, K.J. / MAAS, M (2021): “Artificial intelligence and crime: A primer for criminologists”, en: Crime Media Culture, Vol. 17(2) 209-233
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• KARPPI, T. (2018): ““The Computer Said So”: On the Ethics, Effectiveness, and Cultural Techniques of Predictive Policing”, en: Social Media + Society, pp. 1-9.
• KITCHIN, R. (2014): The Data Revolution: Big Data, Open Data, Data Infrastructures & their Consequences, Sage, London.
• LOHR, S, (2015): Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else, HarperCollins. New York.
• LOINAZ, I. (2017): Manual de evaluación del riesgo de violencia. Metodología y ámbitos de aplicación, Pirámide, Madrid.
• LOWRIE, I. (2017): “Algorithmic rationality: Epistemology and efficiency in the data sciences”, en: Big Data & Society Vol. 4(1), pp. 1-13.
• LUPTON, D. (2013): Risk, Routledge, London.
• LUPTON, D. (2016): The Quantified Self: A Sociology of Self-Tracking, Cambridge, Polity Press.
• MACKENZIE, A. (2005): The Performativity of Code. Software and Cultures of Circulation, Theory, Culture & Society, Vol. 22(1), pp. 71–92.
• MACKENZIE, A. (2015): “The production of prediction: What does machine learning want?”, en European Journal of Cultural Studies, Vol. 18(4-5), pp. 429-445.
• MANTELLO, P. (2016): "The machine that ate bad people: The ontopolitics of the precrime assemblage”, en: Big Data & Society, pp. 1-11
• MAURUTTO, P. / HANNAH-MOFFAT, K. (2006): “Assembling risk and the restructuring of penal control”, en: British Journal of Criminology, Vol. 46(3), pp. 438-454.
• MARTÍNEZ GARAY, L.: (2014): “La incertidumbre de los pronósticos de peligrosidad: consecuencias para la dogmática de las medidas de seguridad”, en: Indret: Revista para el Análisis del Derecho, nº 2, pp. 1-78.
• MARTÍNEZ GARAY, L. / MONTES SUAY, F. (2018): “El uso de valoraciones del riesgo de violencia en Derecho Penal: algunas cautelas necesarias”, en: Indret: Revista para el Análisis del Derecho, nº 2, pp. 1-48.
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• MEHOZAY, Y. / FISHER, E. (2019): “The epistemology of algorithmic risk assessment and the path towards a non-penology penology”, en: Punishment and Society, Vol.21(5), pp. 523-541.
• MINISTRY OF JUSTICE (2010): Breaking the Cycle: Effective Punishment, Rehabilitation and Sentencing of Offenders, Ministry of Justice, London.
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