Incertidumbres algorítmicas en torno a las violencias de géneroEl caso del sistema VioGén y otros sistemas de predicción del riesgo

Referencias bibliográficas

• Álvarez Freijo, M., Andrés-Pueyo, A., Augé Gomà, M., Choy Vilana, A., Fernández Velasco, R., Fernández Rodríguez, C., Foulon, H., López Ferré, S., Martínez-Izquierdo, M.T., Martínez Bueno, C., Saiz-Lloret, M., y Serratusell Salvadó, L. (2011). Protocolo de valoración del riesgo de violencia contra la mujer por parte de su pareja o ex pareja (RVD-BCN). Circuito Barcelona contra la Violencia hacia las Mujeres. https://bcnroc.ajuntament.barcelona.cat/jspui/bitstream/11703/91573/1/10964.pdf
• Angwin, J., Larson, J., Mattu, S. y Kirchner, L. (2016, 23 de mayo) Machine bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
• Ashford, L. J., Spivak, B. L., y Shepherd, S. M. (2022). Racial fairness in violence risk instruments: a review of the literature. Psychology, Crime & Law, 28(9), 911-941. https://doi.org/10.1080/1068316X.2021.1972108
• Barabas C., Dinakar K., Ito, J., Virza, M. y Zittrain J. (2017). Interventions over predictions: Reframing the ethical debate for actuarial risk assessment. Cornell University. https://doi.org/10.48550/arXiv.1712.08238
• Berk, R. A., Kuchibhotla, A. K. y Tchetgen Tchetgen, E. (2023). Fair Risk Algorithms. Annual Review of Statistics and Its Application, 10, 165-187. https://doi.org/10.1146/annurev-statistics-033021-120649
• Berk, R.A., Heidari, H., Jabbari, S., Kearns, M. y Roth, A. (2021). Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research, 50(1), 3-44. https://doi.org/10.1177/0049124118782533
• Berk, R.A. (2012). Criminal justice forecasts of risk: A machine learning approach. Springer Science & Business Media.
• Bodelón González, E., Barcons Campmajó, M., Ortiz Amaro, L., Murillo Blasco, E., Pisonero Francisco, A. y González Pardo, P. (2019, diciembre). Órdenes de protección y derechos de las mujeres que han sufrido violencia de género: obstáculos para una efectiva protección. El Institut Català de les Dones (ICD). https://dones.gencat.cat/web/.content/03_ambits/docs/cdoc_publicacions_ordenes_proteccion.pdf
• Buolamwini, J. y Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 1-15. http://proceedings.mlr.press/v81/buolamwini18a.html
• Campbell, J. C., O’Sullivan, C., Rohel, J. y Webster, D. (2005). Intimate Partner Violence Risk Assessment Validation Study. Final Report. National Institute of Justice (NIJ). https://www.ojp.gov/ncjrs/virtual-library/abstracts/intimate-partner-violence-risk-assessment-validation-study-final
• Campbell, J. C., Webster, D. W. y Glass, N. (2009). The danger assessment: Validation of a lethality risk assessment instrument for intimate partner femicide, Journal of Interpersonal Violence 24(4), 653-674. https://doi.org/10.1177/0886260508317180
• Europa Press (2022, febrero 5). La comisaria de Viogén sobre las pulseras: "Las pondría a todas con riesgo de homicidio" [Entrevista]. https://www.europapress.es/videos/video-comisaria-viogen-pulseras-pondria-todas-riesgo-homicidio-20230205132136.html
• Chalkley, R. y Strang, H. (2017). Predicting domestic homicides and serious violence in Dorset: A replication of Thornton’s Thames Valley analysis. Cambridge Journal of Evidence-Based Policing, 1(2), 81-92. https://doi.org/10.1007/s41887-017-0010-2
• Copas, J. y Marshall, P. (1998). The offender group reconviction scale: A statistical reconviction score for use by probation officers, Journal of the Royal Statistical Society. Series C (Applied Statistics), 47(1), 159–171. https://doi.org/10.1111/1467-9876.00104.
• Davies, H., McKernan, B. y Sabbagh, D. (2023, 9 de diciembre). 'El Evangelio': cómo utiliza Israel la inteligencia artificial para seleccionar objetivos en Gaza. The Guardian/El diario.es [traducción de Francisco de Zárate]. https://www.eldiario.es/internacional/theguardian/evangelio-utiliza-israel-inteligencia-artificial-seleccionar-objetivos-gaza_1_10740704.html
• Dawes, R. M., Faust, D. y Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 243(4899), 1668-1674. https://doi.org/10.1126/science.2648573
• De la Poza, E., Jódar, L. y Barreda, S. (2016). Mathematical modeling of hidden intimate partner violence in Spain: A quantitative and qualitative approach. Abstract and Applied Analysis. https://doi.org/10.1155/2016/8372493
• Delgadillo-Alemán, S., Ku-Carrillo, R., Pérez-Amezcua, B. y Chen-Charpentier, B. (2019). A mathematical model for intimate partner violence. Mathematical and Computational Applications, 24(1), 29. https://doi.org/10.3390/mca24010029
• Desmarais, S., Garrett, B. y Rudin, C. (2019). Risk assessment tools are not a failed ’minority report’. Law360. https://www.law360.com/access-to-justice/articles/1180373/risk-assessment-tools-are-not-a-failed-minority-report-
• Dutton, D. G. y Kropp, P. R. (2000). A review of domestic violence risk instruments. Trauma, Violence & Abuse, 1(2), 171-181. https://doi.org/10.1177/1524838000001002004
• Echeburúa, E., Amor, P. J., Loinaz, I. y De Corral, P. (2010). Escala de Predicción del Riesgo de Violencia Grave contra la pareja-Revisada-(EPV-R). Psicothema, 22(4), 1054-1060. http://www.redalyc.org/articulo.oa?id=72715515077
• Echeburúa, E., Fernández-Montalvo, J., Corral, P. y López-Goñi, J.J. (2009). Assessing risk markers in intimate partner femicide and severe violence. A new assessment instrument. Journal of Interpersonal Violence, 24(6), 925-939. https://doi.org/10.1177/0886260508319370
• Englezos, E. (2023). Policing by algorithm: NSW Police’s Suspect Target Management Plan. Alternative Law Journal, 48(1), 17-24. https://doi.org/10.1177/1037969X221147745
• Ericson, R. V. (1997[1948]). Policing the risk society. University of Toronto Press. http://search.ebscohost.com/login.aspx?direct=truescope= sitedb=e000xnaAN=683064
• Eticas (2022). The external audit of the VioGén system. Association Eticas Research and Innovation https://eticasfoundation.org/wp-content/uploads/2022/04/ETICAS-_-Auditori%CC%81a-Externa-del-sistema-VioGe%CC%81n-_-20220308.docx.pdf
• Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
• European Institute for Gender Equality (EIGE) (2019). Risk assessment and management of intimate partner violence in the EU. Publications Office of the European Union. https://doi.org/10.2839/37233
• Farrington, D. P. y Tarling, R. (1985). Criminological prediction: An introduction. En D.P. Farrington y R. Tarling (Eds.), Prediction in criminology (pp. 2-33). SUNY Press.
• Fernández Teruelo, J. G., Fernández-Rivera González, P. y García Amez, Javier (Dir.) (2022). Nuevas formas de prevención y respuesta jurídico-social frente a la violencia de género. Thomson Reuters Aranzadi.
• Fiscalía General del Estado (2019). Memoria de la Fiscalía General del Estado, 2018. https://www.fiscal.es/memorias/memoria2019/FISCALIA_SITE/index.html
• Flores, A. W., Bechtel, K., y Lowenkamp, C. T. (2016). False positives, false negatives, and false analyses: A rejoinder to machine bias: There's software used across the country to predict future criminals and it's biased against blacks. Federal Probation Journal, 80(2), 38-46. https://www.uscourts.gov/sites/default/files/80_2_6_0.pdf
• García Martín, J., y Rubio, E. (2023, 7 de diciembre). Repunte de asesinatos machistas - Se revisarán los criterios para dar de baja un caso en VioGén [Reportaje]. https://www.rtve.es/v/7030197/?t=55s
• González-Prieto, Á., Brú, A., Nuño, J. C. y González-Álvarez, J. L. (2021). Machine learning for risk assessment in gender-based crime. Computers and Society, arXiv preprint arXiv, 2106.11847. https://doi.org/10.48550/arXiv.2106.11847
• Gottfredson, S. D. y Moriarty, L. J. (2006). Statistical risk assessment: Old problems and new applications. Crime & Delinquency, 52(1), 178-200. https://doi.org/10.1177/0011128705281748
• Graham, L. M., Sahay, K. M., Rizo, C. F., Messing, J. T. y Macy, R. J. (2021). The Validity and Reliability of Available Intimate Partner Homicide and Reassault Risk Assessment Tools: A Systematic Review. Trauma, Violence, & Abuse, 22(1), 18-40. https://doi.org/10.1177/1524838018821952
• Grogger, J., Gupta, S., Ivandic, R. y Kirchmaier, T. (2021). Comparing Conventional and Machine‐Learning Approaches to Risk Assessment in Domestic Abuse Cases. Journal of Empirical Legal Studies, 18(1), 90-130. https://www.nber.org/system/files/working_papers/w28293/w28293.pdf
• Grove, W. M. y Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical–statistical controversy. Psychology, Public Policy, and Law, 2(2), 293-323. https://doi.org/10.1037/1076-8971.2.2.293
• Hannah-Moffat, K. (2019). Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates. Theoretical Criminology, 23(4), 453-470. https://doi.org/10.1177/1362480618763582
• Harcourt, B.E. (2015). Risk as a proxy for race: The dangers of risk assessment. Federal Sentencing Reporter, 27(4), 237–243. https://doi.org/10.1525/fsr.2015.27.4.237
• Harcourt, B.E. (2006). Against prediction: Profiling, policing and punishing in an actuarial age. University of Chicago Press.
• Hassein, N. (2017, 15 de agosto). Against Black Inclusion in Facial Recognition. Digital Talking Drum. https://digitaltalkingdrum.com/2017/08/15/against-black-inclusion-in-facial-recognition/
• Her Majesty’s Inspectorate of Constabulary (2014). Everyone’s business: Improving the police response to domestic abuse. Home Secretary Inspectorate of Constabulary (HMIC). https://www.scie-socialcareonline.org.uk/everyones-business-improving-the-police-response-to-domestic-abuse/r/ a11G0000 003FG 3FIAW
• Jiménez, A., y Douhaibi, A. (2023). The Islamophobic consensus: Datafying racism in Catalonia. En Z. Bednarz, y M. Zalnieriute (Eds.), Money, Power, and AI: Automated Banks and Automated States (pp. 152-170). Cambridge University Press. https://doi.org/10.1017/9781009334297.012
• Kahneman, D., Rosenfield, A. M., Gandhi, L. y Blaser, T. (2016). Noise: How to overcome the high, hidden cost of inconsistent decision making. Harvard Business Review, 94(10), 38-46. https://hbr.org/2016/10/noise
• Kleinberg, J., Mullainathan, S. y Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. En Proceedings of the 8th Conference on Innovations in Theoretical Computer Science.
• Kropp, P R. (2004). Some questions regarding spousal assault risk assessment. Violence against women, 10(6), 676-697. https://doi.org/10.1177/1077801204265019
• Larson, J., Mattu, S., Kirchner, L. y Angwin, J. (2016, 23 de mayo). How We Analyzed the COMPAS Recidivism Algorithm. ProPublica. https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm
• Lin, J., y Chen, C. (2023). The epistemic ethical concerns involving algorithms in intelligent communication. Teknokultura. Revista de Cultura Digital y Movimientos Sociales, 20(Special Issue), 27-36. https://doi.org/10.5209/tekn.87552
• Lo, P. (2022). From data criminalization to prison abolition. Community Justice Exchange Project. https://abolishdatacrim.org/en
• López-Flores, L. M. (2022). Colonialidad algorítmica: Racialización y sexualización mecanizada en el capitalismo digital: Una perspectiva de género. Teknokultura. Revista de Cultura Digital y Movimientos Sociales, 19(2), 113-122. https://doi.org/10.5209/TEKN.78681
• López-Ossorio, J. J., González-Álvarez, J. L., Loinaz, I., Martínez-Martínez, A. y Pineda, D. (2021). Intimate partner homicide risk assessment by police in Spain: The Dual Protocol VPR5.0-H. Psychosocial Intervention, 30(1), 47-55. http://dx.doi.org/10.5093/pi2020a16
• López-Ossorio, J. J., González-Álvarez, J. L., Muñoz Vicente, J. M., Urruela Cortés, C. y Andrés-Pueyo, A. (2019). Validation and calibration of the Spanish police intimate partner violence risk assessment system (VioGén). Journal of police and criminal psychology, 34, 439-449. https://doi.org/10.1007/s11896-019-09322-9
• López-Ossorio, J. J., González-Álvarez, J. L. y Andrés-Pueyo, A. (2016). Predictive effectiveness of the police risk assessment in intimate partner violence. Psychosocial Intervention, 25(1), 1-7. https://doi.org/10.1016/j.psi.2015.10.002
• McNamara, D., Graham, T., Broad, E. y Ong, C. S. (2019). Trade-offs in algorithmic risk assessment: An Australian domestic violence case study. En A. Daly, K. Devitt y M. Mann (Eds.) Good data (pp. 96-116). Institute of Network Cultures.
• Messing, J. Th. y Thaller, J. (2013). The average predictive validity of intimate partner violence risk assessment instruments. Journal of Interpersonal Violence, 28(7), 1537-1558. https://doi.org/10.1177/0886260512468250
• Ministerio del Interior (2022, 28 de julio). VioGén cumple 15 años con más 700.000 casos analizados y 5,4 millones de valoraciones de riesgo realizadas. https://n9.cl/wm98b
• Ministerio del Interior (2019). Guía de procedimiento VPR5.0 y VPER4.1. Protocolo de valoración policial del riesgo y gestión de la seguridad de las víctimas de violencia de género. https://violenciadegenerotic.files.wordpress.com/2019/05/instruccion-4-2019.pdf
• Mitchell, S., Potash E., Barocas S., D’Amour A, Lum. K. (2021). Algorithmic Fairness: Choices, Assumptions, and Definitions. Annual Review of Statistics and Its Applications, 8, 141-163. https://doi.org/10.1146/annurev-statistics-042720-125902
• Muñoz-Vicente, J. M. M. y López-Ossorio, J. J. (2016). Valoración psicológica del riesgo de violencia: alcance y limitaciones para su uso en el contexto forense. Anuario de Psicología Jurídica, 26(1), 130-140. https://doi.org/10.1016/j.apj.2016.04.005
• Noble, S. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
• Northpointe (2015). A Practitioner's Guide to COMPAS Core. Northpointe. https://assets.documentcloud.org/documents/2840784/Practitioner-s-Guide-to-COMPAS-Core.pdf
• Olaciregui, M. de la P. (2021). Prevenir la violencia contra las mujeres: análisis de las herramientas de evaluación y gestión del riesgo desde una perspectiva de género [Tesis doctoral]. Universidad de Zaragoza. https://zaguan.unizar.es/record/109559
• Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
• Pineda, D., Rico-Bordera, P., Galán, M., Piqueras, J. A., and González-Álvarez, J. L. (2023). Women Victims of Intimate Partner Violence and Intimate Partner Homicide: A Typology Based on Victimization Variables. Psychosocial Intervention, 32(1), 43-53. https://doi.org/10.5093/pi2023a3
• Pueyo, A. A. y Redondo, S. (2007). Predicción de la violencia: Entre la peligrosidad y la valoración del riesgo de violencia. Papeles del Psicólogo, 28(3), 157-173. https://www.papelesdelpsicologo.es/pdf/1500.pdf
• Ramos-Torre, R., y Callejo-Gallego, J. (2018). “Semántica social del riesgo: una aproximación cualitativa”, Política y Sociedad, 55(1), 235-256. https:// doi.org/10.5209/POSO.54062
• Richards, L. (2009). Domestic Abuse, Stalking and Harassment and Honour Based Violence (DASH, 2009) Risk Identification and Assessment and Management Model. Association of Police Officers (ACPO). https://reducingtherisk.org.uk/wp-content/uploads/2022/08/DASH-2009.pdf
• Robinson, A. L., Myhill, A., Wire, J., Roberts, J. y Tilley, N. (2016). Risk-led policing of domestic abuse and the DASH risk model. Cardiff University, College of Policing y UCL Department of Security and Crime Science. https://library.college.police.uk/docs/college-of-policing/Risk-led-policing-2-2016.pdf
• Rodríguez-Rodríguez, I., Rodríguez, J. V., Pardo-Quiles, D. J., Heras-González, P. y Chatzigiannakis, I. (2020). Modeling and forecasting gender-based violence through machine learning techniques. Applied Sciences, 10(22), 8244. https://www.mdpi.com/2076-3417/10/22/8244/htm
• Santos-Hermoso, J., González-Álvarez, J.L., Alcázar-Córcoles, M.Á. y Carbonell-Vayá, E. J. (2022). Intimate Partner Homicide Against Women Typology: Risk Factor Interaction in Spain. European Journal on Criminal Policy and Research. https://doi.org/10.1007/s10610-022-09517-7
• Simón-Castellano, P. (2021). Inteligencia artificial y Administración de Justicia: ¿Quo vadis, justitia? IDP. Revista de Internet, Derecho y Política, 33. https://doi.org/10.7238/idp.v0i33.373817
• Skeem, J. y Lowenkamp, C. (2020). Using algorithms to address trade‐offs inherent in predicting recidivism. Behavioral Sciences & the Law, 38(3), 259-278. https://doi.org/10.1002/bsl.2465
• Soler, S. (2023, 7 de diciembre). Interior revisará los criterios que descartan a las mujeres del sistema de protección de violencia de género. https://www.rtve.es/noticias/20231207/interior-igualdad-reunion-violencia-genero/2465163.shtml
• Taylor, K.-Y. (2019). Predatory inclusion. N+1, 35. https://nplusonemag.com/issue-35/essays/predatory-inclusion/
• Thornton, S. (2017). Police attempts to predict domestic murder and serious assaults: Is early warning possible yet? Cambridge Journal of Evidence-Based Policing, 1(2), 64-80. https://doi.org/10.1007/s41887-017-0011-1
• van Der Put, C. E., Gubbels, J., y Assink, M. (2019). Predicting domestic violence: A meta-analysis on the predictive validity of risk assessment tools. Aggression and Violent Behavior, 47, 100-116. https://doi.org/10.1016/j.avb.2019.03.008
• Vignola-Lévesque, C. y Léveillée, S. (2022). Intimate Partner Violence and Intimate Partner Homicide: Development of a Typology Based on Psychosocial Characteristics. Journal of Interpersonal Violence, 37, 17-18, NP15874-NP15898. https://doi.org/10.1177/08862605211021989
• Wang, C., Han, B., Patel, B., y Rudin, C. (2022). In pursuit of interpretable, fair and accurate machine learning for criminal recidivism prediction. Journal of Quantitative Criminology, https://doi.org/10.1007/s10940-022-09545-w
• West, S.M., Whittaker, M., y Crawford, K. (2019). Discriminating systems: Gender, race, and power in AI (Informe técnico). AI Now Institute. https://ainowinstitute.org/discriminatingsystems.pdf
• Zambrano-Guerrero, C. A., y Rodríguez-Pabón, D. M. (2021). Design thinking como herramienta para prevenir la violencia basada en género en estudiantes universitarios. Revista Guillermo de Ockham, 19(2), 293-306. https://doi.org/10.21500/22563202.5316
• Završnik, A. (2021). Algorithmic justice: Algorithms and big data in criminal justice settings. European Journal of Criminology, 18(5), 623-642. https://doi.org/10.1177/1477370819876762
• Zeng, J., Ustun, B., y Rudin, C. (2017). Interpretable classification models for recidivism prediction. Journal of the Royal Statistical Society: Series A (Statistics in Society), 180(3), 689-722. https://doi.org/10.1111/rssa.12227
• Zweig, J. (2010). Extraordinary conditions of release under the bail reform act. Harvard Journal on Legislation, 47, 555–585. https://n9.cl/phutj