Skip to main content
Book cover

Technology Development for Security Practitioners pp 121–133Cite as

COPKIT: Technology and Knowledge for Early Warning/Early Action-Led Policing in Fighting Organised Crime and Terrorism

Part of the Security Informatics and Law Enforcement book series (SILE)

Abstract

Intelligence-led policing methods and supporting analysis tools represent the state-of-the-art approach in analysing, investigating, mitigating and preventing crime. This chapter examines the question of how such methods and tools can address the lack of interaction between long-term high-level strategic intelligence and operational intelligence in the context of the fight against organised crime and terrorism.

First, this study argues that increased complexity of intelligence work requires new approaches extending existing methods by increasing the capability to combine intelligence analysis performed at strategic and operational levels. The new approach realises the required cross-fertilisation by the fusion and exchange of information from different data sources and the incorporation of knowledge resulting from different analysis levels. Second, the capabilities and desirable characteristics of relevant supporting tools for the new “Early Warning, Early Action” (EW/EA) approach are presented. Finally, the chapter discusses corresponding legal, ethical and societal implications of such tools.

The presentation of the EW/EA paradigm and the related supporting tools in this chapter are based on research, inter alia, undertaken in the context of the EU-funded COPKIT project. COPKIT addresses innovative means of fighting organised crime and criminal use of ICT. The project aims to improve the analysing capabilities of LEAs not only during investigation but also preparedness, mitigation and prevention.

Keywords

  • Intelligence-led policing
  • Early warning
  • Early action
  • Artificial intelligence
  • Ethics
  • Data protection

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-69460-9_7
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-69460-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Hardcover Book
USD   119.99
Price excludes VAT (USA)
Learn about institutional subscriptions

References

  1. Huber, N. (2019). Intelligence-Led Policing for Law Enforcement Managers. Available on https://leb.fbi.gov/articles/featured-articles/intelligence-led-policing-for-law-enforcementmanagers. last accessed 15 July 2020.

  2. Baker, T. (2009). Intelligence-led policing: Leadership, strategies, and tactics. Flushing, NY: Looseleaf Law Publications.

    Google Scholar 

  3. Smith, A. et al. (1997). Intelligence-Led Policing: International Perspectives on Policing in the 21st Century. Available online: https://www.ialeia.org/docs/ILP_intl_perspectives.pdf. Last access 15 July 2020.

  4. Goldstein, H. (2003). On further developing problem-oriented policing: The Most critical need, the major impediments, and a proposal. In J. Knutsson (Ed.), Crime prevention studies:Vol. 15. Problem-oriented policing. From innovation to mainstream (pp. 13–57). Monsey, Devon: Criminal Justice Press; Willan Pub.

    Google Scholar 

  5. Peterson, M. (2005). Intelligence-Led Policing: The New Intelligence Architecture. Available online: https://polis.osce.org/file/4811/download?token=rUkdXg7n. Last accessed 15 July 2020.

  6. Clark, R. (2004). Intelligence analysis: A target-centric approach. Washington, D.C.: CQ Press.

    Google Scholar 

  7. Jardines, E. (2005). Using open source effectively: Hearings before the Subcommitee on Intelligence, Information and Terrorsim Risk Assement; Committee on Homeland Security. Available on https://www.govinfo.gov/content/pkg/CHRG-109hhrg24962/html/CHRG-109hhrg24962.htm. Last accessed 15 July 2020.

  8. Quarmby, N. (2003). Futures Work in Strategic Criminal Intelligence: Research Paper presented at the Evaluation in Crime and Justice: Trends and Methods Conference. Available on http://docplayer.net/2599648-Futures-work-in-strategic-criminal-intelligence-neil-quarmby-australian-crime-commission.html. Last access 15 July 2020.

  9. Criminal Intelligence Service Canada (CISC) Strategic Early Warning for Criminal Intelligence: Theoretical Framework and Sentinel Methodology. (2007). Available online https://pdfs.semanticscholar.org/8107/ce7733c54223f058c1c594de3ff583b70dfc.pdf?_ga=2.2826352.1761634195.1594833830-2147085485.1594833830, last accessed 15 July 2020.

  10. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. New York: Wiley.

    MATH  Google Scholar 

  11. Salakhutdinov, R. (2019). Integrating Domain-Knowledge into Deep Learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19). ACM, New York, NY, USA, 3176–3176. https://doi.org/10.1145/3292500.3340416.

  12. Baumgartner, K., Ferrari, S., & Palermo, G. (2008). Constructing Bayesian networks for criminal profiling from limited data. Knowledge-Based Systems, 21, 563–572. https://doi.org/10.1016/j.knosys.2008.03.019.

    CrossRef  Google Scholar 

  13. Gunning, D. (2017). Explainable artificial intelligence (XAI), Darpa/I2O, Program Update November 2017. Available at: https://www.darpa.mil/attachments/XAIProgramUpdate.pdf, last Accessed 26 July 2019.

  14. Mittelstadt, B., Russell, C., & Wachter, S. (2019). Explaining explanations in AI. In Proceedings of the conference on fairness, accountability, and transparency (FAT* '19) (pp. 279–288). New York, NY, USA: ACM.

    CrossRef  Google Scholar 

  15. Tulio Ribeiro, M., Singh, S., & Guestrin, C. (2016). ““why should I trust you?”: Explaining the predictions of any classifier” in proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (KDD '16) (pp. 1135–1144). New York, NY, USA: ACM.

    Google Scholar 

  16. Pavlin, G., Quillinan, T., Mignet, F. & de Oude, Patrick. (2013). “Exploiting Intelligence for National Security” in Strategic Intelligence Management (Eds Babak Akhgar, Simeon Yates), (pp. 181–198).

    Google Scholar 

  17. Policy Connect: Building ethical data policies for the public good. (2019). Available online: https://www.policyconnect.org.uk/sites/site_pc/files/report/1214/fieldreportdownload/raa35577ipcldatatechethicsreportlsinglepagesl0519.pdflast accessed: 15 July 2019.

  18. Norwegian Board of Technology. (2015). Predictive policing can data analysis help the police to be in the right place at the right time?.

    Google Scholar 

  19. Edwards, L., & Urquhart, L. (2016). Privacy in public spaces: What expectations of privacy do we have in social media intelligence? International Journal of Law and Information Technology, 24(3), 279–310.

    CrossRef  Google Scholar 

  20. Lammerant, H., & De Hert, P. (2016). Predictive profiling and its legal limits: Effectiveness gone forever. In Exploring the boundaries of big data (pp. 145–173). Amsterdam University Press/WRR.

    Google Scholar 

  21. Babuta, A. (2017). Big data and policing: An assessment of law enforcement requirements, expectations and priorities. In Royal United Services Institute for Defence and security studies.

    Google Scholar 

  22. Sanders, C. B., & Sheptycki, J. (2017). Policing, crime and ‘big data’; towards a critique of the moral economy of stochastic governance. Crime, Law and Social Change, 68(1–2), 1–15.

    CrossRef  Google Scholar 

  23. Couchman, H. (2019). Policing by Machine: Predictive policing and a threat to our rights. Liberty. Available on https://www.libertyhumanrights.org.uk/sites/default/files/LIB%2011%20Predictive%20Policing%20Report%20WEB.pdf, last accessed 11 July 2019.

  24. Barrett, Lindsey, Reasonably Suspicious Algorithms. (2017). Predictive policing at the United States Border. 41 N.Y.U. Review of Law & Social Change, 327–363.

    Google Scholar 

  25. Ferguson, A. G. (2012). Predictive policing and reasonable suspicion. Emory LJ, 62, 259.

    Google Scholar 

  26. Rinik, C., Oswald, M., & Babuta, A. (2019). Machine learning algorithms and police decision-making: Legal. Ethical and Regulatory Challenges.

    Google Scholar 

  27. Kirkpatrick, K. (2017). It's not the algorithm, it's the data. Communications of the ACM, 60(2), 21–23.

    CrossRef  MathSciNet  Google Scholar 

  28. Ethics Committee, West Midlands Police and Crime Commissionaire: Notes of meeting held Wednesday 03 April 2019 (2019). Available online: https://www.westmidlandspcc.gov.uk/ethics-committee/, last accessed 11 July 2019.

  29. European Commission. Policy: Artificial Intelligence, available on https://ec.europa.eu/digital-single-market/en/artificial-intelligence, last accessed 26 November 2019.

  30. College of Policing. Code of Ethics: A Code of Practice for the Principles and Standards of Professional Behaviour for the Policing Profession of England and Wales. (2014). Available on https://www.college.police.uk/What-we-do/Ethics/Documents/Code_of_Ethics.pdf, last accessed 11 July 2019.

  31. GDPR Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance).

    Google Scholar 

  32. Robinson, D. and Koepke, L., 2016. Stuck in a Pattern. Early evidence on predictive policing.

    Google Scholar 

Download references

Acknowledgement

This chapter is based on research, inter alia, undertaken in the context of the EU-funded COPKIT project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 786687. The views expressed in this chapter are those of the authors alone and are in no way intended to reflect those of the European Commission.

Author information

Authors and Affiliations

  1. Ingeniería de Sistemas para la Defensa de España, Madrid, Spain

    Raquel Pastor

  2. Thales Netherlands, Delft, The Netherlands

    Franck Mignet

  3. Bavarian University for Policing, Fürstenfeldbruck, Germany

    Tobias Mattes & Holger Nitsch

  4. Trilateral Research Ltd, London, UK

    Agata Gurzawska & David Wright

Authors
  1. Raquel Pastor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Franck Mignet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tobias Mattes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Agata Gurzawska
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Holger Nitsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raquel Pastor .

Editor information

Editors and Affiliations

  1. CENTRIC (Centre of Excellence in Terrorism, Resilience, Intelligence and Organised Crime Research), Sheffield Hallam University, Sheffield, UK

    Dr. Babak Akhgar

  2. Center for Security Studies-KEMEA, Athens, Greece

    Dimitrios Kavallieros

  3. Crisis Management and Security Unit, Institute of Communication and Computer, Athens, Greece

    Dr. Evangelos Sdongos

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Pastor, R., Mignet, F., Mattes, T., Gurzawska, A., Nitsch, H., Wright, D. (2021). COPKIT: Technology and Knowledge for Early Warning/Early Action-Led Policing in Fighting Organised Crime and Terrorism. In: Akhgar, B., Kavallieros, D., Sdongos, E. (eds) Technology Development for Security Practitioners. Security Informatics and Law Enforcement. Springer, Cham. https://doi.org/10.1007/978-3-030-69460-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-69460-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69459-3

  • Online ISBN: 978-3-030-69460-9

  • eBook Packages: EngineeringEngineering (R0)