Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-25T21:10:21.312Z Has data issue: false hasContentIssue false
  • English
  • Français

Machine-Learnt Bias? Algorithmic Decision Making and Access to Criminal Justice

Overall Winner, Justis International Law & Technology Writing Competition 2020, by Malwina Anna Wojcik of the University of Bologna

Published online by Cambridge University Press:  16 September 2020

Malwina Anna Wojcik
Get access Rights & Permissions [Opens in a new window]

Extract

The pressure on the criminal justice system in England and Wales is mounting. Recent figures reveal that despite a rise in recorded crime, the number of defendants in court proceedings has been the lowest in 50 years. This indicates a crisis of access to criminal justice. Predictive policing and risk assessment programmes based on algorithmic decision making (ADM) offer a prospect of increasing efficiency of law enforcement, eliminating delays and cutting the costs. These technologies are already used in the UK for crime-mapping and facilitating decisions regarding prosecution of arrested individuals. In the US their deployment is much wider, covering also sentencing and parole applications.

Type
Shorter Articles
Information
Legal Information Management , Volume 20 , Issue 2 , June 2020 , pp. 99 - 100
Copyright
Copyright © The Author(s) 2020. Published by British and Irish Association of Law Librarians

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Footnotes

1 ‘Numbers dealt with in justice system at 50-year low’ (The Times, 15 November 2019), <https://www.thetimes.co.uk/article/numbers-dealt-with-in-justice-system-at-50-year-low-k5hnb50kd> accessed 15 November 2019.

2 For example: PredPol used by Kent Police between 2013 and 2018 or MapInfo used by West Midlands Police.

3 For example: Harm Assessment Risk Tool (HART). See: The Law Society Commission on the Use of Algorithms in the Justice System, Algorithms in the Criminal Justice System (June 2019) para 7.3.1.

4 For example: Correctional Offender Management Profiling for Alternative Sanctions (COMPAS). See: ‘Practitioner's Guide to COMPAS Core’ (Equivant 2019) < http://www.equivant.com/wp-content/uploads/Practitioners-Guide-to-COMPAS-Core-040419.pdf> accessed 15 November 2019.

5 J Kleinberg et al, ‘Human Decisions and Machine Predictions. (2018) Quarterly Journal of Economics 133, 237.

6 J Buolamwini, ‘Compassion through Computation: Fighting Algorithmic Bias’ (speech at the 2019 World Economic Forum in Davos) <https://www.youtube.com/watch?v=_sgji-Bladk> accessed 15 November 2019.

7 Chiao, V, ‘Fairness, Accountability and Transparency: Notes on Algorithmic Decision-making in Criminal Justice’ (2019) 15 International Journal of Law in Context 126CrossRefGoogle Scholar, 127.

8 J Angwin et al, 2016, ‘Machine Bias’ (ProPublica, 23 May 2016) <https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing> accessed 15 November 2019.

9 H Couchman, ‘Policing by Machine. Predictive Policing and the Threat to our Rights’ (Liberty, January 2019) 15.

10 Art. 5(1)(c) ECHR.

11 The European Parliament Research Service, Understanding algorithmic decision-making: Opportunities and challenges (March 2019) 46.

12 ibid 55.

13 Chiao (n 7) 129.

14 The Law Society Commission (n 3) para 8.4.

15 R Feloni, ‘An MIT Researcher who Analyzed Facial Recognition Software Found Eliminating Bias in AI is a Matter of Priorities’ (Business Insider, 23 January 2019) < https://www.businessinsider.sg/biases-ethics-facial-recognition-ai-mit-joy-buolamwini-2019-1/> accessed 15 November 2019.

16 The Law Society Commission (n 3) para 8.2, sub-recommendation 4.3.

17 ibid para 8.3, sub-recommendation 1.7.

18 ibid para 8.4, sub-recommendation 3.1.

19 ibid para 8.3, sub-recommendation 4.4.