Abstract
As domestic abuse has become a higher priority for law enforcement in England and Wales, so demand and the intensity of resource deployment has increased. With many police struggling to meet demand, some are exploring algorithms as a means to better predict the risk of serious harm and so better target their resources. In this chapter, I set out the case for algorithms playing a role in domestic abuse strategies, within the context of their wider growth in policing. I include examples of how targeting algorithms work now and explore a range of concerns and potential pitfalls. The central argument of this chapter is to promote the cause of regulation in algorithms in policing. This fledgling field has much promise but will not succeed without due regard to the many potential problems that accompany it.
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- 1.
In this chapter I use a loose definition of serious to reflect homicide, serious assault, serious sexual offences and coercive and controlling behaviour. This definition is not a statutory one or formed with any kind of harm index. More detail on severity is included in [7].
References
Agrawal A, Gans J, Goldfarb A (2018) Prediction machines: the simple economics of artificial intelligence. Harvard Business Press
Barnes G, Hyatt JM (2012) Classifying adult probationers by forecasting future offending
Barnham L, Barnes GC, Sherman LW (2017) Targeting escalation of intimate partner violence: evidence from 52,000 offenders. Camb J Evid Based Polic, 1–27
Berk R (2012) Criminal justice forecasts of risk: a machine learning approach. Springer Science & Business Media
Berk RA, Sorenson SB, Barnes G (2016) Forecasting domestic violence: a machine learning approach to help inform arraignment decisions. J Empir Leg Stud 13(1):94–115
Bland M, Ariel B (2015) Targeting escalation in reported domestic abuse: Evidence from 36,000 callouts. Int Crim Justice Rev 25(1):30–53. https://doi.org/10.1177/1057567715574382
Bland MP (2020). Targeting domestic abuse by mining police records. Doctoral dissertation, University of Cambridge
Brauneis R, Goodman EP (2018) Algorithmic transparency for the smart city. Yale JL & Tech, 20, p 103
Carlo S (2017) Artificial intelligence, big data and the rule of law, event report. The Bingham centre for the rule of law, 9 October 2017 https://www.biicl.org/event/1280
Carrell SE, Hoekstra M (2012) Family business or social problem? The cost of unreported domestic violence. J Policy Anal Manag 31(4):861–875
Chalkley R, Strang H (2017) Predicting domestic homicides and serious violence in Dorset: A replication of Thornton’s Thames Valley analysis. Camb J Evid Based Polic 1(2–3):81–92
Ensign D, Friedler SA, Neville S, Scheidegger C, Venkatasubramanian S (2017) Runaway feedback loops in predictive policing. arXiv:1706.09847
Giroux HA (2015) Totalitarian paranoia in the post-Orwellian surveillance state. Cult Stud 29(2):108–140
Gracia E (2004) Unreported cases of domestic violence against women: towards an epidemiology of social silence, tolerance, and inhibition. J Epidemiol Commun Health 58(7): 536–537
Greengard S (2012) Policing the future. Commun ACM 55(3):19–21
Grogger J, Ivandic R, Kirchmaier T (2020) Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases (CEP Discussion Paper No 1676 February 2020)
Hern A (2020) What is facial recognition-and how to police use it? The Guardian. https://www.theguardian.com/technology/2020/jan/24/what-is-facial-recognition-and-how-do-police-use-it. Accessed on 6 March 2020
Hickman L (2013) How algorithms rule the world. The Guardian. Accessed on 5 March 2020
Her Majesty’s Inspectorate of the Constabulary, Fire and Rescue Services (2014a) Everyone’s business: Improving the police response to domestic violence. https://www.justiceinspectorates.gov.uk/hmicfrs/wp-content/uploads/2014/04/improving-the-police-response-to-domestic-abuse.pdf. Accessed 15 Oct 2016
Her Majesty’s Inspectorate of the Constabulary, Fire and Rescue Services (2014b) Crime recording: making the victim count. https://www.justiceinspectorates.gov.uk/hmicfrs/wp-content/uploads/crime-recording-making-the-victim-count.pdf. Accessed 15 Oct 2016
Her Majesty’s Inspectorate of Constabulary, Fire and Rescue Services (2018) The state of policing: The annual assessment of policing in England and Wales. https://www.justiceinspectorates.gov.uk/hmicfrs/wp-content/uploads/state-of-policing-2017-2.pdf. Accessed 5 March 2020
Howgego J (2019) A UK police force is dropping tricky cases on advice of an algorithm. the New Scientist. https://www.newscientist.com/article/2189986-a-uk-police-force-is-dropping-tricky-cases-on-advice-of-an-algorithm/Accessed 6 March 2020
Joh EE (2017) Artificial intelligence and policing: First questions. Seattle UL Rev 41:1139
Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349(6245):255–260
Kahneman D (2011) Thinking, fast and slow. Macmillan
Kerr J, Whyte C, Strang H (2017) Targeting escalation and harm in intimate partner violence: evidence from northern territory police, Australia. Camb J Evid Based Polic 1–17
Kropp PR (2004) Some questions regarding spousal assault risk assessment. Violence against Women 10(6):676–697
Liberty (2019) Liberty report exposes police forces’ use of discriminatory data to predict crime. https://www.libertyhumanrights.org.uk/news/press-releases-and-statements/liberty-report-exposes-police-forces’-use-discriminatory-data-0. Accessed 4 March 2019
McFadzien K, Phillips JM (2019) Perils of the subjective approach: A critical analysis of the UK national crime recording standards. Polic J Policy Pract
Meehl P (1954) Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. University of Minnesota Press, Minneapolis
Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L (2016) The ethics of algorithms: Mapping the debate. Big Data Soc 3(2):2053951716679679
Mohler GO, Short MB, Brantingham PJ, Schoenberg FP, Tita GE (2011) Self-exciting point process modeling of crime. J Am Stat Assoc 106(493):100–108
Myhill A (2015) Measuring coercive control: What can we learn from national population surveys? Violence against Women 21(3):355–375
Nillson P (2018) First UK police force to try predictive policing ends contract. Financial Times. https://www.ft.com/content/b34b0b08-ef19-11e8-89c8-d36339d835c0. Accessed 6 March 2020
Nix J (2015) Predictive policing. Critical issues in policing: Contemporary readings, p 275
Olhede S, Wolfe P (2017) When algorithms go wrong, who is liable? Significance 14(6):8–9
Olhede SC, Wolfe PJ (2018) The growing ubiquity of algorithms in society: implications, impacts and innovations. Philos Trans R Soc A Math Phy Eng Sci 376(2128):20170364
Office for National Statistics (ONS) (2017) Domestic abuse in England and Wales: year ending March 2017. Statistical Bulletin. London, UK: Office of National Statistics. https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/bulletins/domesticabuseinenglandandwales/yearendingmarch2017. Accessed 17 March 2018
Office for National Statistics (ONS) (2018) Domestic abuse in England and Wales: year ending March 2018. Statistical Bulletin. London, UK: Office of National Statistics. https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/bulletins/domesticabuseinenglandandwales/yearendingmarch2018. Accessed 2 March 2019
Office for National Statistics (ONS) (2019) Domestic abuse in England and Wales: year ending March 2018. Statistical Bulletin. London, UK: Office of National Statistics. https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/bulletins/domesticabuseinenglandandwales/yearendingmarch2019 Accessed 6 March 2020
Oswald M, Grace J (2016) Intelligence, policing and the use of algorithmic analysis: a freedom of information-based study. J Inf Rights Policy Pract 1(1)
Oswald M, Grace J, Urwin S, Barnes GC (2018) Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘experimental’ proportionality. Inf Commun Technol Law 27(2):223–250
Phua C, Alahakoon D, Lee V (2004) Minority report in fraud detection: classification of skewed data. ACM SIGKDD Explorations Newsl 6(1):50–59
Ratcliffe J (2015) What is the future… of predictive policing. Practice 6(2):151–166
Richards L, Letchford S, Stratton S (2008) Policing Domestic Violence. Oxford University Press, Oxford. Blackstone’s Practical Policing
Robinson AL, Myhill A, Wire J, Roberts J, Tilley N (2016) Risk-led policing of domestic abuse and the DASH risk model. What works: crime Reduction Research. Cardiff & London: Cardiff University, College of Policing and UCL Department of Security and Crime Science
Saunders J, Hunt P, Hollywood JS (2016) Predictions put into practice: a quasi-experimental evaluation of Chicago’s predictive policing pilot. J Exp Criminol 12(3):347–371
Shmueli G, Ray S, Estrada JMV, Chatla SB (2016) The elephant in the room: Predictive performance of PLS models. J Bus Res 69(10):4552–4564
Sherman LW (2013) The rise of evidence-based policing: Targeting, testing, and tracking. Crime Justice 42(1):377–451
Simmons R (2017) Big Data and Procedural Justice: Legitimizing Algorithms in the Criminal Justice System. Ohio St J Crim L 15:573
Stark E (2007) Coercive control: How men entrap women in everyday life. Oxford University Press, New York, NY
Stroud M (2014) The minority report: Chicago’s new police computer predicts crimes, but is it racist. The Verge, 19
Tankebe J (2013) Viewing things differently: The dimensions of public perceptions of police legitimacy. Criminology 51(1):103–135
Thornton S (2017) Police Attempts to predict domestic murder and serious assaults: is early warning possible yet? Camb J Evid Based Polic 1–17
Tulumello S (2016) The long way to a safer Memphis: Local policies for crime prevention need structural change. Benjamin L. Hooks Institute for Social Change Policy Papers, pp 12–22
Turner E, Medina J, Brown G (2019) Dashing hopes? The predictive accuracy of domestic abuse risk assessment by the police, Brit J Criminol, azy074
Tyler TR (2004) Enhancing police legitimacy. Annal Am Acad Polit Soc Science 593(1):84–99
Urwin S (2016) Algorithmic forecasting of offender dangerousness for police custody officers: an assessment of accuracy for the Durham constabulary model: Master Thesis. University of Cambridge. Wolfson College
Vlahos J (2012) The department of pre-crime. Sci Am 306(1): 62–67
Willocks L (2019) Are we facing AI Armageddon? What’s wrong with the automation and future of work debate. Forbes. https://www.forbes.com/sites/londonschoolofeconomics/2019/08/08/are-we-facing-ai-armageddon-whats-wrong-with-the-automation-and-future-of-work-debate/#461f909a314b. Accessed 6 March 2020
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Bland, M. (2020). Algorithms Can Predict Domestic Abuse, But Should We Let Them?. In: Jahankhani, H., Akhgar, B., Cochrane, P., Dastbaz, M. (eds) Policing in the Era of AI and Smart Societies. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-50613-1_6
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