Algoritmos de valoración de riesgo Contexto, concepto y limitaciones
Barra lateral del artículo
Publicado:
27-11-2023
Contenido principal del artículo
Vol. 44 Núm. Ext. (2023): Inteligencia artificial y sistema penal, Artículos doctrinales, Páginas 1-29
Recibido: 30-11-2022
Aceptado: 08-03-2023
Publicado: 27-11-2023
Resumen
La noción de riesgo ha tenido, desde siempre, un papel fundamental en la configuración de los modelos de penalidad en el Norte Global. Su irrupción, desarrollo y expansión responde a un proceso multicausal de enorme complejidad que ha acompañado y acompaña de forma permanente a las ciencias penales
Llevando esto a la actualidad, cuestiones como el Big Data¸ el Machine Learning o los nuevos paradigmas político-criminales parecen dibujar un escenario para el que el modelo de penalidad, al menos en términos de valoración riesgo, no parece estar preparado. En este contexto, surgen los Algoritmos de Valoración de Riesgo (ARA) en tanto que herramientas capaces de ofrecer potenciales soluciones. Los ARA plantean a través de nuevas premisas, métodos y tecnologías un nuevo horizonte de lo posible en el que son capaces de predecir más y mejor que sus predecesores.
Así las cosas, este trabajo tiene por objetivo revisar, desde una perspectiva crítica, todo lo recién expuesto en tanto que forma de intentar constatar la veracidad de las premisas de que los ARA son capaces de superar a todos los instrumentos anteriores así como de que son la mejor forma de avanzar hacia un modelo de penalidad mejor.
Palabras clave:
Algoritmos de Valoración de Riesgo; Riesgo; Big Data; Machine Learning; PolíticaCriminal
Citado por
Detalles del artículo
Referencias
• ANEESH, A. (2009): “Global Labor: Algocratic Modes of Organization”, en: Sociological Theory, Vol. 27(4), pp. 347-370.
• ARADAU, C. / BLANKE, T. (2017): “Politics of prediction: Security and the time/space of governmentality in the age of Big Data”, en: European Journal of Social Theory, Vol. 20(3), pp. 373-391.
• BARRY J. (2016): “Bio-fuelling the hummer? Transdisciplinary thoughts on techno-optimism and innovation in the transition from unsustainability”, en: BYRNE E. / MULLALLY G. / SAGE C. (eds.), Transdisciplinary perspectives on transitions to sustainability, Routledge, Abingdon.
• BECCARIA, C. (1998/1764): De los delitos y las penas, Alianza Editorial, Madrid.
• BEER, D. (2016): “How should we do the history of Big Data?”, en: Big Data & Society, pp. 1-10
• BENBOUZID, B. (2019): “To predict and to manage. Predictive policing in the United States”, en: Big Data & Society, pp. 1-13.
• BENNETT MOSES, L. / CHAN, J. (2014): “Using Big Data for Legal and Law Enforcement Decisions: Testing the New Tools”, en: University of New South Wales Law Journal, Vol. 37 (2), 2014, pp. 643-678.
• BERK, R.A. / BLEICH, J. (2013): “Statistical procedures for forecasting criminal behavior: A comparative assessment”, en Criminology & Public Policy, Vol. 12(3), pp. 513-544.
• BOYD, D. / CRAWFORD, K. (2012): “Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon”, en: Information, Communication and Society, Vol. 15(5), pp. 662-679.
• BRANDARIZ GARCÍA, J.A. (2016), El modelo gerencial-actuarial de penalidad. Eficiencia, riesgo y sistema penal, Dykinson, Madrid.
• BRAYNE, S. (2017): “Big Data Surveillance: The Case of Policing”, en: American Sociological Review, Vol. 82(5), pp. 977-1008
• BRENNAN, T. / OLIVER, W.L. (2013): “The emergence of machine learning techniques in criminology”, en Criminology & Public Policy, Vol.12(3), pp. 551–562.
• CAPLAN, J.M. ET AL. (2021): “Data-Informed and Place-Based Violent Crime Prevention: The Kansas City, Missouri Risk-Based Policing Initiative”, en: Police Quarterly, Vol. 0(0), pp. 1-27.
• CARUANA, R. / NICULESCU-MIZIL, A. (2006) “An empirical comparison of supervised learning algorithms using different performance metrics”, en Proceedings of the 23rd International Conference on Machine Learning, pp. 161-168.
• CHAN, J. / BENNETT MOSES, L. (2016): “Is Big Data challenging criminology?”, en: Theoretical Criminology, Vol. 20(1), pp. 21-39.
• DANAHER, J. (2016): “The threat of algocracy: Reality, resistance and accommodation”, en: Philosophy and Technology, Vol. 29(3), pp. 245–268.
• DANAHER, J. ET AL. (2017): “Algorithmic governance: Developing a research agenda through the power of collective intelligence”, en: Big Data and Society¸ pp. 1-21.
• DIAKOPOULOS N. (2014): “Algorithmic-Accountability: The investigation of Black Boxes Report”, en: Tow Center for Digital Journalism, Columbia University, pp. 1-37.
• DIETER, M.S. (2013): Política Criminal Atuarial. A Criminología do fim da Història, Revan, Rio de Janeiro.
• DORON, C.O. (2016): “The experience of “risk”. Genealogy and transformations”, en BURGESS, A. / ALEMANNO, A. / ZINN, J.O., (eds.), Routledge Handbook of Risk Studies, Routledge, New York, pp. 17-26.
• KEHL, D. / GUO, P. / KESSLER, S. (2017): “Algorithms in the Criminal Justice System: Assessing the Use of Risk Assessments in Sentencing”, en: Responsive Communities Initiative, Berkman Klein Center for Internet & Society, Harvard Law School, pp. 1-37.
• DOUGLAS, M. (1994): (1994), Risk and Blame. Essays in cultural theory, Routledge, London.
• DUWE, G. / KIM, K. (2017): “Out with the old and in with the new? An empirical comparison of supervised learning algorithms to predict recidivism”, en: Criminal Justice Policy Review, Vol. 28(6), pp. 570–600.
• ECKHOUSE, L. ET AL. (2019): “Layers of Bias. A Unified Approach for Understanding Problems With Risk Assessment”, en: Criminal Justice and Behavior, Vol. 46 (2), pp. 185-209.
• ERICSON, R.V. / DOYLE, A. (2003): “Risk and Morality”, en: ERICSON, R. / DOYLE A. (eds.) Risk and Morality, University of Toronto Press, Toronto, pp. 1-10.
• FEELEY, M.M. / SIMON, J. (1992): “The New Penology: Notes on the emerging srategy of corrections and its implications”, en Criminology, Vol. 30(4), pp. 449-474.
• FEELEY, M.M. / SIMON, J. (1994): “Actuarial Justice: The Emerging New Criminal Law”, en: NELKEN, D. (ed.), The Futures of Criminology, Sage, London, pp. 173-201.
• FERGUSON, A.G. (2015): “Big Data and predictive reasonable suspicion”, en: University of Pennsylvania Law Review, Vol.163(2), pp. 327-410.
• FOWLES, J. (1974): “On Chronocentrism”, en: Futures, Vol. 6(1), pp. 65-68.
• FUKUYAMA, F. (1992/1992): “El fin de la Historia y el último hombre”¸ Planeta, Barcelona.
• GALLOWAY, K. (2017): “Big Data: A case study of disruption and government power” en: Alternative Law Journal, Vol. 42(2), pp. 89-95.
• GARB, H.N. / WOOD, J.M. (2019): “Methodological advances in statistical prediction”, en: Psychological Assessment, Vol. 31(12), pp.1456-1466.
• GARLAND, D. (2001): Mass Imprisonment: Social Causes and Consequences, Sage, London
• GARLAND, D, (2003): “The Rise of Risk”, en: ERICSON, R. / DOYLE A, (eds.), Risk and Morality, University of Toronto Press, Toronto, pp. 48-86.
• GIDDENS, A, (1999): “Risk and Responsibility”, en: The Modern Law Review, Vol. 62(1), pp. 1-10.
• GODDARD, T. / MYERS, R.R. (2017): “Against evidence-based oppression: Marginalized youth and the politics of risk-based assessment and intervention”, en: Theoretical Criminology, Vol. 21(2), pp. 151-167
• HACKING, I. (1991): “How should we do the history of statistics?”, en: BURCHELL, G. / GORDON, C. / MILLER, P. (eds), The Foucault Effect Chicago, The University of Chicago Press, Chicago, pp. 181–195.
• 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
• HOFFMAN, P. (2003): History of the Federal Parole System, U.S. Dept. of Justice, United States Parole Commission, Washington, D.C.
• HOME OFFICE (2006): A Five Year Strategy for Protecting the Public and Reducing Reoffending. Command Number 6717, The Stationery Office, London.
• JURGENSON, N. (2014): View from nowhere. The New Inquiry. http://thenewinquiry.com/essays/view-from-nowhere/. (Último acceso: 11/2022).
• 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.
• MAYER-SCHÖNBERGER V / CULKIER K. (2013): Big Data: A Revolution That Will Transform How We Live, Work and Think, John Murray, London.
• 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.
• MINOCHER, X. / RANDALL, C. (2020): “Predictable policing: New technology, old bias, and future resistance in Big Data surveillance”, en: Convergence: The International Journal of Research into New Media Technologies, Vol. 26(5-6), pp. 1108–1124.
• MURPHY, K.P. (2012): Machine learning: A probabilistic perspective. The MIT Press, Cambridge.
• O´MALLEY, P. (1994): “Neo-liberal crime control. Political agendas and the future of crime prevention in Australia”, en: CHAPPELL, D. / WILSON, P. (eds.), The Australian Criminal Justice System. The Mid 1990s, Butterworths, Sydney, pp. 283-298.
• PASQUALE, F. (2015): The Black Box Society: The Secret Algorithms That Control Money and Information, Harvard University Press, Cambridge.
• PETERSILIA, J. (1997): “Probation in the United States”, en Crime and Justice, nº 22, pp. 149-200.
• PETERSILIA, J. (1998): Community corrections: probation, parole, and intermediate sanctions, Oxford University Press, New York.
• POLACK, P. (2020): “Beyond algorithmic reformism: Forward engineering the designs of algorithmic systems”, en: Big Data & Society, pp. 1-15
• PHELPS, M.S. (2017): “Mass probation: Toward a more robust theory of state variation in punishment”, en: Punishment & Society, Vol. 19(1), pp. 53-73.
• REICHMAN, N. (1986): “Managing Crime Risks: Towards an Insurance Based Model of Social Control”, en Research in Law and Social Control, nº 8, pp. 151-172.
• RESNYANSKY, L. (2019): “Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge”, en: Big Data & Society, pp. 1-12
• RIVERA BEIRAS, I. (2015): “Actuarialismo penitenciario. Su recepción en España”: en: Revista Crítica Penal y Poder 2015, nº 9, pp.102-144.
• RIGAKOS, G.S. / HADDEN, R.W. (2001): “Crime, Capitalism and the «Risk Society»”: Towards the same olde modernity?”, en: Theoretical Criminology, Vol. 5(1), pp. 61-84.
• ROUVROY, A. / BERNS, T. (2013): “Algorithmic governmentality and prospects of emancipation. Disparateness as a precondition for individuation through relationships?”, en: Réseaux, Vol. 177(1), pp.163-.196.
• ROBINSON DG (2018): “The challenges of prediction: Lessons from criminal justice” en: I/S: A Journal of Law and Policy for the Information Society, Vol. 14(2), pp. 151-186.
• SIMON, J. (1987): “The emergence of risk society: Insurance, Law and the State”, en Socialist Review, Vol. 95(1), pp. 61-89.
• SIMON, J. (1993): Poor discipline: parole and the social control of the underclass, 1890- 1990, University of Chicago, Chicago.
• SIMON, J. (2013): “Punishment and the Political Technologies of the Body”, en SIMON, J. / SPARKS, R., (eds.), The Sage Handbook of Punishment and Society, Sage, London, pp. 60-89.
• SIMON, J. / FEELEY, M.M. (2003): “The Form and Limits of the New Penology”, en BLOMBERG, T.G. / COHEN, S., (eds.), Punishment and Social Control, Aldine de Gruyter, New York, pp. 76-116.
• SOLAR CAYÓN, J.I. (2020): “Inteligencia artificial en la justicia penal: los sistemas algorítmicos de evaluación de riesgos” en SOLAR CAYÓN, J.I. (ed.) Dimensiones éticas y jurídicas de la inteligencia artificial en el marco del Estado de Derecho, Universidad de Alcalá, Alcalá de Henares, pp. 125-172
• STARR, S.B. (2014): “Evidence-based sentencing and the scientific rationalization of discrimination”, en Stanford Law Review, Vol. 66(4), pp. 803-872.
• STARR, S.B (2015): (2015), “The New Profiling. Why Punishing Based on Poverty and Identity Is Unconstitutional and Wrong”, en: Federal Sentencing Reporter, Vol. 27(4), pp. 229-236.
• STEVENSON, M.T. / SLOBOGIN, C. (2018): “Algorithmic Risk Assessments and the Double-Edged Sword of Youth” en: Washington University Law Review, Vol. 96(3), pp. 681-706.
• STRIKWERDA, L. (2021): “Predictive policing: The risks associated with risk assessment”, en: The Police Journal: Theory, Practice and Principles, Vol. 94(3) 422-436
• STRIPHAS, T. (2015): “Algorithmic culture”, en: European Journal of Cultural Studies, Vol. 18(4-5), pp. 395-412.
• TOLLENAAR, N. / VAN DER HEIJDEN, P.G.M. (2013): “Which method predicts recidivism best? A comparison of statistical, machine learning and data mining predictive models”, en: Journal of the Royal Statistical Society, Vol. 176 (2), pp. 565–584.
• UGWUDIKE, P. (2020): “Digital prediction technologies in the justice system: The implications of a ‘race-neutral’ agenda” en: Theoretical Criminology, Vol. 24(3), pp. 482-501.
• VANSTONE, M. (2004): “Mission control: The origins of a humanitarian service”, en Probation Journal, Vol. 51(1), pp. 34-47.
• VAN EIJK, G. (2017): “Socioeconomic marginality in sentencing: The built-in bias in risk assessment tools and the reproduction of social inequality”, en: Punishment & Society, Vol. 19(4), pp. 463-481.
• VELÁSQUEZ, J, (2014): “El origen del paradigma de riesgo”, en Política Criminal, Vol. 9(17), pp. 58-117.
• WHITE, S. (1976): “The nineteenth century origins of pre-sentence reports”, en Australian and New Zealand Journal of Criminology, Vol. 11(3), pp. 157-178.
• WEXLER, R. (2018): “Life, liberty, and trade secrets: Intellectual property in the criminal justice system” en: Stanford Law Review, Vol. 70, pp. 1343-1429.
• ZAVRSNIK, A. (2021): “Algorithmic justice: Algorithmics and Big Data in criminal justice settings”, en: European Journal of Criminology, Vol. 18(5), pp. 623-642.
• ARADAU, C. / BLANKE, T. (2017): “Politics of prediction: Security and the time/space of governmentality in the age of Big Data”, en: European Journal of Social Theory, Vol. 20(3), pp. 373-391.
• BARRY J. (2016): “Bio-fuelling the hummer? Transdisciplinary thoughts on techno-optimism and innovation in the transition from unsustainability”, en: BYRNE E. / MULLALLY G. / SAGE C. (eds.), Transdisciplinary perspectives on transitions to sustainability, Routledge, Abingdon.
• BECCARIA, C. (1998/1764): De los delitos y las penas, Alianza Editorial, Madrid.
• BEER, D. (2016): “How should we do the history of Big Data?”, en: Big Data & Society, pp. 1-10
• BENBOUZID, B. (2019): “To predict and to manage. Predictive policing in the United States”, en: Big Data & Society, pp. 1-13.
• BENNETT MOSES, L. / CHAN, J. (2014): “Using Big Data for Legal and Law Enforcement Decisions: Testing the New Tools”, en: University of New South Wales Law Journal, Vol. 37 (2), 2014, pp. 643-678.
• BERK, R.A. / BLEICH, J. (2013): “Statistical procedures for forecasting criminal behavior: A comparative assessment”, en Criminology & Public Policy, Vol. 12(3), pp. 513-544.
• BOYD, D. / CRAWFORD, K. (2012): “Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon”, en: Information, Communication and Society, Vol. 15(5), pp. 662-679.
• BRANDARIZ GARCÍA, J.A. (2016), El modelo gerencial-actuarial de penalidad. Eficiencia, riesgo y sistema penal, Dykinson, Madrid.
• BRAYNE, S. (2017): “Big Data Surveillance: The Case of Policing”, en: American Sociological Review, Vol. 82(5), pp. 977-1008
• BRENNAN, T. / OLIVER, W.L. (2013): “The emergence of machine learning techniques in criminology”, en Criminology & Public Policy, Vol.12(3), pp. 551–562.
• CAPLAN, J.M. ET AL. (2021): “Data-Informed and Place-Based Violent Crime Prevention: The Kansas City, Missouri Risk-Based Policing Initiative”, en: Police Quarterly, Vol. 0(0), pp. 1-27.
• CARUANA, R. / NICULESCU-MIZIL, A. (2006) “An empirical comparison of supervised learning algorithms using different performance metrics”, en Proceedings of the 23rd International Conference on Machine Learning, pp. 161-168.
• CHAN, J. / BENNETT MOSES, L. (2016): “Is Big Data challenging criminology?”, en: Theoretical Criminology, Vol. 20(1), pp. 21-39.
• DANAHER, J. (2016): “The threat of algocracy: Reality, resistance and accommodation”, en: Philosophy and Technology, Vol. 29(3), pp. 245–268.
• DANAHER, J. ET AL. (2017): “Algorithmic governance: Developing a research agenda through the power of collective intelligence”, en: Big Data and Society¸ pp. 1-21.
• DIAKOPOULOS N. (2014): “Algorithmic-Accountability: The investigation of Black Boxes Report”, en: Tow Center for Digital Journalism, Columbia University, pp. 1-37.
• DIETER, M.S. (2013): Política Criminal Atuarial. A Criminología do fim da Història, Revan, Rio de Janeiro.
• DORON, C.O. (2016): “The experience of “risk”. Genealogy and transformations”, en BURGESS, A. / ALEMANNO, A. / ZINN, J.O., (eds.), Routledge Handbook of Risk Studies, Routledge, New York, pp. 17-26.
• KEHL, D. / GUO, P. / KESSLER, S. (2017): “Algorithms in the Criminal Justice System: Assessing the Use of Risk Assessments in Sentencing”, en: Responsive Communities Initiative, Berkman Klein Center for Internet & Society, Harvard Law School, pp. 1-37.
• DOUGLAS, M. (1994): (1994), Risk and Blame. Essays in cultural theory, Routledge, London.
• DUWE, G. / KIM, K. (2017): “Out with the old and in with the new? An empirical comparison of supervised learning algorithms to predict recidivism”, en: Criminal Justice Policy Review, Vol. 28(6), pp. 570–600.
• ECKHOUSE, L. ET AL. (2019): “Layers of Bias. A Unified Approach for Understanding Problems With Risk Assessment”, en: Criminal Justice and Behavior, Vol. 46 (2), pp. 185-209.
• ERICSON, R.V. / DOYLE, A. (2003): “Risk and Morality”, en: ERICSON, R. / DOYLE A. (eds.) Risk and Morality, University of Toronto Press, Toronto, pp. 1-10.
• FEELEY, M.M. / SIMON, J. (1992): “The New Penology: Notes on the emerging srategy of corrections and its implications”, en Criminology, Vol. 30(4), pp. 449-474.
• FEELEY, M.M. / SIMON, J. (1994): “Actuarial Justice: The Emerging New Criminal Law”, en: NELKEN, D. (ed.), The Futures of Criminology, Sage, London, pp. 173-201.
• FERGUSON, A.G. (2015): “Big Data and predictive reasonable suspicion”, en: University of Pennsylvania Law Review, Vol.163(2), pp. 327-410.
• FOWLES, J. (1974): “On Chronocentrism”, en: Futures, Vol. 6(1), pp. 65-68.
• FUKUYAMA, F. (1992/1992): “El fin de la Historia y el último hombre”¸ Planeta, Barcelona.
• GALLOWAY, K. (2017): “Big Data: A case study of disruption and government power” en: Alternative Law Journal, Vol. 42(2), pp. 89-95.
• GARB, H.N. / WOOD, J.M. (2019): “Methodological advances in statistical prediction”, en: Psychological Assessment, Vol. 31(12), pp.1456-1466.
• GARLAND, D. (2001): Mass Imprisonment: Social Causes and Consequences, Sage, London
• GARLAND, D, (2003): “The Rise of Risk”, en: ERICSON, R. / DOYLE A, (eds.), Risk and Morality, University of Toronto Press, Toronto, pp. 48-86.
• GIDDENS, A, (1999): “Risk and Responsibility”, en: The Modern Law Review, Vol. 62(1), pp. 1-10.
• GODDARD, T. / MYERS, R.R. (2017): “Against evidence-based oppression: Marginalized youth and the politics of risk-based assessment and intervention”, en: Theoretical Criminology, Vol. 21(2), pp. 151-167
• HACKING, I. (1991): “How should we do the history of statistics?”, en: BURCHELL, G. / GORDON, C. / MILLER, P. (eds), The Foucault Effect Chicago, The University of Chicago Press, Chicago, pp. 181–195.
• 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
• HOFFMAN, P. (2003): History of the Federal Parole System, U.S. Dept. of Justice, United States Parole Commission, Washington, D.C.
• HOME OFFICE (2006): A Five Year Strategy for Protecting the Public and Reducing Reoffending. Command Number 6717, The Stationery Office, London.
• JURGENSON, N. (2014): View from nowhere. The New Inquiry. http://thenewinquiry.com/essays/view-from-nowhere/. (Último acceso: 11/2022).
• 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.
• MAYER-SCHÖNBERGER V / CULKIER K. (2013): Big Data: A Revolution That Will Transform How We Live, Work and Think, John Murray, London.
• 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.
• MINOCHER, X. / RANDALL, C. (2020): “Predictable policing: New technology, old bias, and future resistance in Big Data surveillance”, en: Convergence: The International Journal of Research into New Media Technologies, Vol. 26(5-6), pp. 1108–1124.
• MURPHY, K.P. (2012): Machine learning: A probabilistic perspective. The MIT Press, Cambridge.
• O´MALLEY, P. (1994): “Neo-liberal crime control. Political agendas and the future of crime prevention in Australia”, en: CHAPPELL, D. / WILSON, P. (eds.), The Australian Criminal Justice System. The Mid 1990s, Butterworths, Sydney, pp. 283-298.
• PASQUALE, F. (2015): The Black Box Society: The Secret Algorithms That Control Money and Information, Harvard University Press, Cambridge.
• PETERSILIA, J. (1997): “Probation in the United States”, en Crime and Justice, nº 22, pp. 149-200.
• PETERSILIA, J. (1998): Community corrections: probation, parole, and intermediate sanctions, Oxford University Press, New York.
• POLACK, P. (2020): “Beyond algorithmic reformism: Forward engineering the designs of algorithmic systems”, en: Big Data & Society, pp. 1-15
• PHELPS, M.S. (2017): “Mass probation: Toward a more robust theory of state variation in punishment”, en: Punishment & Society, Vol. 19(1), pp. 53-73.
• REICHMAN, N. (1986): “Managing Crime Risks: Towards an Insurance Based Model of Social Control”, en Research in Law and Social Control, nº 8, pp. 151-172.
• RESNYANSKY, L. (2019): “Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge”, en: Big Data & Society, pp. 1-12
• RIVERA BEIRAS, I. (2015): “Actuarialismo penitenciario. Su recepción en España”: en: Revista Crítica Penal y Poder 2015, nº 9, pp.102-144.
• RIGAKOS, G.S. / HADDEN, R.W. (2001): “Crime, Capitalism and the «Risk Society»”: Towards the same olde modernity?”, en: Theoretical Criminology, Vol. 5(1), pp. 61-84.
• ROUVROY, A. / BERNS, T. (2013): “Algorithmic governmentality and prospects of emancipation. Disparateness as a precondition for individuation through relationships?”, en: Réseaux, Vol. 177(1), pp.163-.196.
• ROBINSON DG (2018): “The challenges of prediction: Lessons from criminal justice” en: I/S: A Journal of Law and Policy for the Information Society, Vol. 14(2), pp. 151-186.
• SIMON, J. (1987): “The emergence of risk society: Insurance, Law and the State”, en Socialist Review, Vol. 95(1), pp. 61-89.
• SIMON, J. (1993): Poor discipline: parole and the social control of the underclass, 1890- 1990, University of Chicago, Chicago.
• SIMON, J. (2013): “Punishment and the Political Technologies of the Body”, en SIMON, J. / SPARKS, R., (eds.), The Sage Handbook of Punishment and Society, Sage, London, pp. 60-89.
• SIMON, J. / FEELEY, M.M. (2003): “The Form and Limits of the New Penology”, en BLOMBERG, T.G. / COHEN, S., (eds.), Punishment and Social Control, Aldine de Gruyter, New York, pp. 76-116.
• SOLAR CAYÓN, J.I. (2020): “Inteligencia artificial en la justicia penal: los sistemas algorítmicos de evaluación de riesgos” en SOLAR CAYÓN, J.I. (ed.) Dimensiones éticas y jurídicas de la inteligencia artificial en el marco del Estado de Derecho, Universidad de Alcalá, Alcalá de Henares, pp. 125-172
• STARR, S.B. (2014): “Evidence-based sentencing and the scientific rationalization of discrimination”, en Stanford Law Review, Vol. 66(4), pp. 803-872.
• STARR, S.B (2015): (2015), “The New Profiling. Why Punishing Based on Poverty and Identity Is Unconstitutional and Wrong”, en: Federal Sentencing Reporter, Vol. 27(4), pp. 229-236.
• STEVENSON, M.T. / SLOBOGIN, C. (2018): “Algorithmic Risk Assessments and the Double-Edged Sword of Youth” en: Washington University Law Review, Vol. 96(3), pp. 681-706.
• STRIKWERDA, L. (2021): “Predictive policing: The risks associated with risk assessment”, en: The Police Journal: Theory, Practice and Principles, Vol. 94(3) 422-436
• STRIPHAS, T. (2015): “Algorithmic culture”, en: European Journal of Cultural Studies, Vol. 18(4-5), pp. 395-412.
• TOLLENAAR, N. / VAN DER HEIJDEN, P.G.M. (2013): “Which method predicts recidivism best? A comparison of statistical, machine learning and data mining predictive models”, en: Journal of the Royal Statistical Society, Vol. 176 (2), pp. 565–584.
• UGWUDIKE, P. (2020): “Digital prediction technologies in the justice system: The implications of a ‘race-neutral’ agenda” en: Theoretical Criminology, Vol. 24(3), pp. 482-501.
• VANSTONE, M. (2004): “Mission control: The origins of a humanitarian service”, en Probation Journal, Vol. 51(1), pp. 34-47.
• VAN EIJK, G. (2017): “Socioeconomic marginality in sentencing: The built-in bias in risk assessment tools and the reproduction of social inequality”, en: Punishment & Society, Vol. 19(4), pp. 463-481.
• VELÁSQUEZ, J, (2014): “El origen del paradigma de riesgo”, en Política Criminal, Vol. 9(17), pp. 58-117.
• WHITE, S. (1976): “The nineteenth century origins of pre-sentence reports”, en Australian and New Zealand Journal of Criminology, Vol. 11(3), pp. 157-178.
• WEXLER, R. (2018): “Life, liberty, and trade secrets: Intellectual property in the criminal justice system” en: Stanford Law Review, Vol. 70, pp. 1343-1429.
• ZAVRSNIK, A. (2021): “Algorithmic justice: Algorithmics and Big Data in criminal justice settings”, en: European Journal of Criminology, Vol. 18(5), pp. 623-642.
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