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Keeping judges in the loop: a human–machine collaboration strategy against the blind spots of AI in criminal justice

  • Data analytics and machine learning
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Abstract

While seeping into every aspect of our lives, intelligent systems carry serious concerns, many of which stem from the need to secure control over machines ensuring they turn into an enhancement and not into a threat to human societies. Issues are not lacking. Whether one considers recruiting platforms perpetuating historical patterns of discrimination or AI-driven systems unfairly evaluating the creditworthiness of natural persons, it clearly appears vital to firmly place human beings with their rights and needs at the center of intelligent technologies. The paper tackles this kind of issue by focusing on the use of artificial intelligence in criminal justice, where predictive analytics and AI-driven decision systems have proven capable not only of enhancing the fight against crime but also of bringing the risk of opacity, aberrations, and injustices. We present a human–machine collaboration strategy providing judges with the advantages of AI and computational heuristics while offering control and understanding of the role played by machines. Drawing from a research that led to the development of an experimental platform supporting judges and public prosecutors dealing with organized crime, the strategy revolves around two components: (i) an online learning model designed to support judges in the evaluation of criminal dangerousness of individuals and groups capable of learning from users’ feedback; (ii) a human–computer interaction component exploiting visual metaphors to ease judges’ interaction with data, AI and other heuristics. The main contribution of the work is a novel and viable declension of the Human-centered AI paradigm in justice administration.

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Data availability

Enquiries about data availability should be directed to the authors.

Notes

  1. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

  2. 2012/C 326/02.

  3. Evidence and facts emerging before the judge frequently call for “contextualized” assessment. For instance, the number or the direction of phone calls between individuals under surveillance is not unequivocally meaningful by itself. It may significantly vary depending on the organization considered, the region and the culture—in a way that only a human being can evaluate.

  4. It is a statistical measure for assessing the reliability of agreement between a fixed number of raters when assigning categorical ratings to a number of items or classifying items; Fleiss’ kappa can range from \(-1\) to \(+1\), with \(-1\) indicating that there was no observed agreement, 0 indicating that agreement was no better than chance, and \(+1\) that indicates perfect agreement.

  5. The choice can be made among individual-phonecall graph, individual-environmental tapping graph, individual crime graph, and combination thereof, as well as individual network.

  6. https://doc.linkurio.us/ogma/latest/.

  7. https://d3js.org.

  8. With regards to the semantic meaning displayed, we have used, for example, influence for the betweenness centrality (\(f_8\) and \(F_{16}\)), popularity for (), gregariousness for ().

  9. We remark that most of the systems PPs use in Italy, like the so called “Consolle del Magistrato” are mainly management tools with tabular visualization, that do not offer enhanced forms of interaction with data nor visual metaphors.

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Funding

This study has received no funding.

Author information

Authors and Affiliations

  1. National Institute for Public Policy Analysis, Corso Italia, 33, 00198, Rome, RM, Italy

    Nicola Lettieri

  2. Dept. of Law, Economics, Management and Quantitative Methods, University of Sannio, Via delle Puglie, 82, 82100, Benevento, BN, Italy

    Nicola Lettieri & Alfonso Guarino

  3. Dept. of Computer Science, University of Salerno, Via Giovanni Paolo II, 130, 84084, Fisciano, SA, Italy

    Rocco Zaccagnino & Delfina Malandrino

Authors
  1. Nicola Lettieri
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  2. Alfonso Guarino
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  3. Rocco Zaccagnino
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  4. Delfina Malandrino
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Contributions

Conceptualization: NL; Methodology: AG, RZ, NL; Formal analysis and investigation: NL; Project administration: DM; Software: Alfonso Guarino; Visualization: NL, DM; Method: AG, RZ; Data curation: NL, DM; Writing Original Draft: NL, AG; Writing Reviewing and Editing: NL, DM, RZ; Supervision: DM, NL.

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Correspondence to Nicola Lettieri.

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Appendices

A Details of crime profile features employed by the online learning model

Here, we provide further details on the features \(f_1\) and \(f_2\) engineered for our AI model concerning the imprisonment months foreseen by the Italian Law for the different crimes.

Table 3 Crimes and corresponding imprisonment period foreseen by the Italian law
Full size table

B Acronyms’ list

Here, we provide the list of acronyms used throughout this paper.

Table 4 Acronyms used in this paper
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Lettieri, N., Guarino, A., Zaccagnino, R. et al. Keeping judges in the loop: a human–machine collaboration strategy against the blind spots of AI in criminal justice. Soft Comput 27, 11275–11293 (2023). https://doi.org/10.1007/s00500-023-08604-z

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