Skip to main content
SpringerLink
Log in

Algorithms Can Predict Domestic Abuse, But Should We Let Them?

  • Chapter
  • First Online:
Policing in the Era of AI and Smart Societies

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 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

  1. Agrawal A, Gans J, Goldfarb A (2018) Prediction machines: the simple economics of artificial intelligence. Harvard Business Press

    Google Scholar 

  2. Barnes G, Hyatt JM (2012) Classifying adult probationers by forecasting future offending

    Google Scholar 

  3. 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

    Google Scholar 

  4. Berk R (2012) Criminal justice forecasts of risk: a machine learning approach. Springer Science & Business Media

    Google Scholar 

  5. 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

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. Bland MP (2020). Targeting domestic abuse by mining police records. Doctoral dissertation, University of Cambridge

    Google Scholar 

  8. Brauneis R, Goodman EP (2018) Algorithmic transparency for the smart city. Yale JL & Tech, 20, p 103

    Google Scholar 

  9. 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

  10. Carrell SE, Hoekstra M (2012) Family business or social problem? The cost of unreported domestic violence. J Policy Anal Manag 31(4):861–875

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. Ensign D, Friedler SA, Neville S, Scheidegger C, Venkatasubramanian S (2017) Runaway feedback loops in predictive policing. arXiv:1706.09847

  13. Giroux HA (2015) Totalitarian paranoia in the post-Orwellian surveillance state. Cult Stud 29(2):108–140

    Article  Google Scholar 

  14. 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

    Google Scholar 

  15. Greengard S (2012) Policing the future. Commun ACM 55(3):19–21

    Article  Google Scholar 

  16. 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)

    Google Scholar 

  17. 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

  18. Hickman L (2013) How algorithms rule the world. The Guardian. Accessed on 5 March 2020

    Google Scholar 

  19. 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

  20. 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

  21. 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

  22. 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

  23. Joh EE (2017) Artificial intelligence and policing: First questions. Seattle UL Rev 41:1139

    Google Scholar 

  24. Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349(6245):255–260

    Article  Google Scholar 

  25. Kahneman D (2011) Thinking, fast and slow. Macmillan

    Google Scholar 

  26. 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

    Google Scholar 

  27. Kropp PR (2004) Some questions regarding spousal assault risk assessment. Violence against Women 10(6):676–697

    Article  Google Scholar 

  28. 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

  29. McFadzien K, Phillips JM (2019) Perils of the subjective approach: A critical analysis of the UK national crime recording standards. Polic J Policy Pract

    Google Scholar 

  30. Meehl P (1954) Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. University of Minnesota Press, Minneapolis

    Book  Google Scholar 

  31. Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L (2016) The ethics of algorithms: Mapping the debate. Big Data Soc 3(2):2053951716679679

    Article  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. Myhill A (2015) Measuring coercive control: What can we learn from national population surveys? Violence against Women 21(3):355–375

    Article  Google Scholar 

  34. 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

  35. Nix J (2015) Predictive policing. Critical issues in policing: Contemporary readings, p 275

    Google Scholar 

  36. Olhede S, Wolfe P (2017) When algorithms go wrong, who is liable? Significance 14(6):8–9

    Article  Google Scholar 

  37. 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

    Article  Google Scholar 

  38. 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

  39. 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

  40. 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

  41. 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)

    Google Scholar 

  42. 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

    Article  Google Scholar 

  43. Phua C, Alahakoon D, Lee V (2004) Minority report in fraud detection: classification of skewed data. ACM SIGKDD Explorations Newsl 6(1):50–59

    Article  Google Scholar 

  44. Ratcliffe J (2015) What is the future… of predictive policing. Practice 6(2):151–166

    Google Scholar 

  45. Richards L, Letchford S, Stratton S (2008) Policing Domestic Violence. Oxford University Press, Oxford. Blackstone’s Practical Policing

    Google Scholar 

  46. 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

    Google Scholar 

  47. 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

    Article  Google Scholar 

  48. 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

    Article  Google Scholar 

  49. Sherman LW (2013) The rise of evidence-based policing: Targeting, testing, and tracking. Crime Justice 42(1):377–451

    Article  Google Scholar 

  50. Simmons R (2017) Big Data and Procedural Justice: Legitimizing Algorithms in the Criminal Justice System. Ohio St J Crim L 15:573

    Google Scholar 

  51. Stark E (2007) Coercive control: How men entrap women in everyday life. Oxford University Press, New York, NY

    Google Scholar 

  52. Stroud M (2014) The minority report: Chicago’s new police computer predicts crimes, but is it racist. The Verge, 19

    Google Scholar 

  53. Tankebe J (2013) Viewing things differently: The dimensions of public perceptions of police legitimacy. Criminology 51(1):103–135

    Article  Google Scholar 

  54. Thornton S (2017) Police Attempts to predict domestic murder and serious assaults: is early warning possible yet? Camb J Evid Based Polic 1–17

    Google Scholar 

  55. 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

    Google Scholar 

  56. Turner E, Medina J, Brown G (2019) Dashing hopes? The predictive accuracy of domestic abuse risk assessment by the police, Brit J Criminol, azy074

    Google Scholar 

  57. Tyler TR (2004) Enhancing police legitimacy. Annal Am Acad Polit Soc Science 593(1):84–99

    Article  Google Scholar 

  58. 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

    Google Scholar 

  59. Vlahos J (2012) The department of pre-crime. Sci Am 306(1): 62–67

    Google Scholar 

  60. 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

Download references

Author information

Authors and Affiliations

  1. Institute of Criminology, University Cambridge, Cambridge, UK

    Matthew Bland

Authors
  1. Matthew Bland
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthew Bland .

Editor information

Editors and Affiliations

  1. International Journal of Electronic Security and Digital Forensics, London Campus, Northumbria University, London, UK

    Hamid Jahankhani

  2. Centre of Excellence in Terrorism, Resilience, Intelligence and Organised Crime Research, Sheffield Hallam University, CENTRIC, Sheffield, South Yorkshire, UK

    Babak Akhgar

  3. Cochrane Associates Limited, University of Suffolk, Ipswich, Suffolk, UK

    Peter Cochrane

  4. Waterfront Building, University of Suffolk, Ipswich, Suffolk, UK

    Mohammad Dastbaz

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50613-1_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50612-4

  • Online ISBN: 978-3-030-50613-1

  • eBook Packages: Law and CriminologyLaw and Criminology (R0)

Publish with us

Policies and ethics

Search

Navigation