Cited by 231Artificial Intelligence in LawEthics and Social Impacts of AIDigitalization, Law, and Regulation

Criminal justice, artificial intelligence systems, and human rights

Open access
Završnik, Aleš·2020·ERA Forum

Abstract

The automation brought about by big data analytics, machine learning and artificial intelligence systems challenges us to reconsider fundamental questions of criminal justice. The article outlines the automation which has taken place in the criminal justice domain and answers the question of what is being automated and who is being replaced thereby. It then analyses encounters between artificial intelligence systems and the law, by considering case law and by analysing some of the human rights affected. The article concludes by offering some thoughts on proposed solutions for remedying the risks posed by artificial intelligence systems in the criminal justice domain.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s12027-020-00602-0

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The deployment of risk scores in criminal and juvenile justice systems has distributive consequences and directly affects institutional legitimacy.

    “This finding shifts the debate to the realm of algorithmic equity, given that the deployment of risk scores in criminal and juvenile justice systems has distributive consequences and directly affects institutional legitimacy (Garrett & Monahan, 2020;Završnik, 2020Završnik, , 2021)).”

    From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Završnik, 2020

    Sections 3.1 and 3.2.1 connect criminal-justice risk assessments and predictive systems to consequential decisions and unequal treatment. The Introduction says opaque AI decision-making can undermine legitimacy, but the copy does not establish that risk scores directly affect institutional legitimacy or address their deployment in juvenile justice. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 12

  1. Brkan, M.: Do Algorithms Rule the World? Algorithmic Decision-Making in the Framework of the GDPR and Beyond. SSRN Scholarly Paper (2017) 66 Ibid. 67 Turkle [36]. 68 Eubanks [9], p. 81. 69 Ibid.
  2. Eubanks, V.: Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, pp. 80-81. St. Martin's Press, New York (2018)
  3. Freeman, K.: Algorithmic injustice: how the Wisconsin Supreme Court failed to protect due process rights in state V. Loomis. NC J. Law Technol. 18(5), 75-106 (2016)
  4. Izdebski, K. (ed.): alGOVrithms-State of Play. ePaństwo Foundation (2019). Available at: https:// epf.org.pl/en/projects/algovrithms/
  5. Kanduč, Z.: Družbena kriza, nacionalna država in 'varnostno vprašanje' v kriminološki perspektivi. J. Crim. Criminol. 62(2), 141-154 (2011)
  6. Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., Mullainathan, S.: Human decisions and ma- chine predictions. Q. J. Econ. 133, 237 (2018). More on positive uses: Sunstein, C.R.: Algorithms, Correcting Biases (December 12, 2018). Social Research. Available at: https://ssrn.com/abstract= 3300171
  7. Pasquale, F.: The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press, Cambridge (2015)
  8. Plesničar, M.M., Završnik, A., Šarf, P.: Fighting impunity with new tools: how big data, algorithms, machine learning and AI shape the new era of criminal justice. In: Marin, L., Montaldo, S. (eds.) The Fight Against Impunity in EU Law. Hart Publishing, Oxford (2020, forthcoming)
  9. Siegel, E.: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, 1st edn. Wiley, Hoboken (2103)
  10. Veale, M., Edwards, L.: Clarity, surprises, and further questions in the Article 29 Working Party draft guidance on automated decision-making and profiling. Comput. Law Secur. Rev. 34(2), 398- 404 (2018)
  11. Wu, X., Zhang, X.: Automated Inference on Criminality using Face Images. arXiv (2016). Available at: http://arxiv.org/abs/1611.04135 · arXiv
  12. Završnik, A.: Algorithmic justice: algorithms and big data in criminal justice settings'. Eur. J. Crimi- nol. (2019). https://doi.org/10.1177/1477370819876762 · DOI

Cited by

Sorted by citations

Authors
Završnik, A.
Venue
ERA Forum
Published
2020-02-20
Volume
20
Issue
4
Pages
567-583
ISSN
  • 1612-3093 (ISSN-L)
  • 1863-9038
OpenAlex
W3007703012
Cited by
231
References
2
Citations by year
2026: 32 · 2025: 39 · 2024: 43 · 2023: 45 · 2022: 40 · 2021: 22 · 2020: 10
FWCI
100.98
Type
journal article
Language
en
Open access
Locations
ERA Forum (publishedVersion, cc-by)
Funders
  • European Commission (HORIZON2020, 734855)
  • Javna Agencija za Raziskovalno Dejavnost RS (V5-1930)
Topics
  • Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
artificial intelligence in criminal justice, human rights, big data analytics, machine learning, algorithmic decision-making, case law, AI regulation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review