Abstract
This chapter examines the use of algorithms in the realm of criminal justice (known as algorithmic criminal justice) and the potential paradigm shift towards pre-emption-driven decision-making. It contributes to debates about the increasing role of enabling technologies in understanding and responding to crime by turning the spotlight on criminal proceedings. It argues that, at first sight, algorithmic decision-making tools may present a strong potential to improve the operational efficiency of criminal justice authorities, but their use remains associated with hard-to-solve challenges, ranging from lack of transparency to questionable compatibility with core principles of substantive and procedural criminal law. Finally, it highlights the need for a balanced dialogue at the crossroads of technological novelty and (criminal) justice.
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Notes
- 1.
- 2.
The term ‘algorithm’ describes a ‘finite sequence of formal rules’ that makes it possible ‘to obtain a result from the initial input of information’: CEPEJ (2018), p. 69. An algorithm ‘may be part of an automated execution process and draw on models designed through machine learning’: Idem.
- 3.
- 4.
Završnik (2019), p. 2. For the purposes of this chapter, the term ‘algorithmic governance’ is understood as ‘a form of social ordering that relies on coordination between actors, is based on rules and incorporates particularly complex computer-based epistemic procedures’: Katzenbach and Ulbricht (2019), p. 2.
- 5.
- 6.
E.g., Aletras et al. (2016).
- 7.
- 8.
CEPEJ (2018), pp. 14, 17–18.
- 9.
Završnik (2019), p. 3.
- 10.
CEPEJ (2020).
- 11.
CEPEJ (2018).
- 12.
- 13.
- 14.
European Commission (EC) (2021) Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. COM (2021) 206 final. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206. This manuscript was submitted for publication in 2021 and, thus, it does not take into account the European Parliament's negotiating position on the AI Act that was released only in June 2023 and marked the beginning of the trilogue negotiations. See European Parliament (2023).
- 15.
Loomis, 881 N.W.2d, para.755 (emphasis added).
- 16.
COMPAS is one of the most commonly used risk-assessment algorithms across the US. See Wisser (2019).
- 17.
Loomis, 881 N.W.2d, para.755; Wisser (2019), p. 1814.
- 18.
Loomis, 881 N.W.2d, para.757; Wisser (2019), p. 1814.
- 19.
Loomis, 881 N.W.2d, para.757 (emphasis added).
- 20.
Summarised by Wisser (2019), p. 1815.
- 21.
- 22.
Quattrocolo (2020), p. 131.
- 23.
Idem; cf. Greenstein (2021).
- 24.
Acknowledging the limits of this book chapter in terms of providing a comprehensive comparative analysis, the focus liese on European jurisdictions that follow the civil law tradition.
- 25.
Quattrocolo (2020), pp. 132–133.
- 26.
Ibid (2020), pp. 137–144.
- 27.
See ibid (2020), pp. 151–152.
- 28.
- 29.
See Lancaster, Lumb (2006).
- 30.
- 31.
- 32.
See Quattrocolo (2020), pp. 181–221.
- 33.
Cf. CEPEJ (2018), pp. 20–25.
- 34.
Cf. Greenstein (2021).
- 35.
- 36.
Melzer (2020), p. 148.
- 37.
- 38.
Melzer (2020), p. 148.
- 39.
- 40.
Cf. Holder (2014).
- 41.
See Papadimitrakis (2019).
- 42.
- 43.
- 44.
Greenstein (2021).
- 45.
FRA (2020), pp. 69–73.
- 46.
Završnik (2019), p. 7.
- 47.
- 48.
- 49.
- 50.
- 51.
- 52.
Kehl et al. (2017), p. 23.
- 53.
Starr (2014), p. 806.
- 54.
Wisser (2019), p. 1818.
- 55.
Mittelstadt et al. (2016), p. 4.
- 56.
Završnik (2019), p. 10.
- 57.
The term “language” may have to be interpreted in a broader way in the future, considering the potential inclusion of programming languages into the respective discourse.
- 58.
Quattrocolo (2020), p. 93.
- 59.
- 60.
- 61.
Cf. FRA (2020), p. 58, 77.
- 62.
Sachoulidou (2021).
- 63.
Wexler (2018), p. 1343.
- 64.
See ECtHR (2020), p. 31.
- 65.
Cf. FRA (2020), p. 76.
- 66.
- 67.
- 68.
Cf. Hannah-Moffat (2019).
- 69.
- 70.
Kehl et al. (2017), pp. 26–27.
- 71.
Završnik (2019), p. 5.
- 72.
Ibid, p.6.
- 73.
Kaiafa-Gbandi (2019).
- 74.
Digital Rights Ireland Ltd v Minister for Communications, Marine and Natural Resources and Others and Kärntner Landesregierung and Others, Joined Cases C-293/12 and C-594/12, CJEU, 8 April 2014, ECLI: ECLI:EU:C:2014:238. See also Privacy International v Secretary of State for Foreign and Commonwealth Affairs and Others, Case C-623/17, CJEU, 6 October 2020, ECLI:EU:C:2020:790; Quadrature du Net and Others v. Premier ministre and Others, Joined Cases C-511/18, C-512/18 and C-520/18, CJEU, 6 October 2020, ECLI: ECLI:EU:C:2020:791.
- 75.
CEPEJ (2018), pp. 8–13.
- 76.
While examining the challenges of prediction in the realm of criminal justice, Robinson (2018, pp. 154–174) identifies three core tensions formulated as follows: ‘What Matters Versus What the Data Measure’; ‘Current Goals Versus Historical Patterns’; and ‘Public Authority Versus Private Expertise’.
- 77.
- 78.
Završnik (2019), p. 9.
- 79.
Ibid, p.11.
- 80.
Idem.
- 81.
See Hildebrandt (2018).
- 82.
- 83.
Završnik (2019), p. 7.
- 84.
See Quattrocolo (2020), pp. 214–215.
- 85.
- 86.
Cf. CEPEJ (2018), p. 36.
- 87.
- 88.
See Hildebrandt (2019).
- 89.
As suggested in the Loomis v. Wisconsin case; cf. Wisser (2019), p. 1816.
- 90.
See Wisser (2019), p. 1829.
- 91.
Cf. ibid, pp.1827–1828; Ebersbach (2020), p. 36.
- 92.
- 93.
Loomis, 881 N.W.2d, para.760.
- 94.
Wisser (2019), p. 1831.
- 95.
Idem.
- 96.
Idem.
- 97.
- 98.
Cf. Završnik (2019), p. 8.
- 99.
EC (2019), p. 1 (emphasis added).
- 100.
Ibid, p. 2.
- 101.
CEPEJ (2018), p. 54.
- 102.
Cf. Završnik (2019), p. 16.
- 103.
- 104.
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Sachoulidou, A. (2023). Algorithmic Criminal Justice: Is It Just a Science Fiction Plot Idea?. In: Kornilakis, A., Nouskalis, G., Pergantis, V., Tzimas, T. (eds) Artificial Intelligence and Normative Challenges. Law, Governance and Technology Series, vol 59. Springer, Cham. https://doi.org/10.1007/978-3-031-41081-9_8
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