# Legal & Ethical Implications of Predictive Digital Techniques in the Judicial Criminal Proceedings

Grazia Garzo, Alessandro Palumbo

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Grazia Garzo, Alessandro Palumbo. Legal & Ethical Implications of Predictive Digital Techniques in the Judicial Criminal Proceedings. ISDFS 2025 - 13th International Symposium on Digital Forensics and Security, Apr 2025, Boston, United States. pp.1-6, 10.1109/isdfs65363.2025.11012003. hal-05021547

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<figure><img src="image_1.png" /></figure>
Legal & Ethical Implications of Predictive Digital
Techniques in the Judicial Criminal Proceedings

Grazia Garzo

University of Siena, Italy

g.garzo@student.unisi.it

Alessandro Palumbo

CentraleSupélec, Inria, Univ Rennes, CNRS, IRISA, France

alessandro.palumbo@inria.fr

***Abstract***—The integration of predictive technologies into the Italian criminal justice system represents a transformative opportunity to enhance judicial efficiency, reduce case processing times, and ensure greater consistency in decisions. These tools, powered by Artificial Intelligence, have the potential to optimize case management and resource allocation, particularly in complex areas such as road homicides, where objective and verifiable factors dominate legal reasoning. However, this shift also raises critical ethical and legal challenges, including risks of algorithmic bias, opacity in decision-making processes, and the potential erosion of judicial autonomy. By focusing on issues such as decision-making consistency, the preservation of judicial discretion, and the legal responsibility associated with AI outputs, this work provides a comprehensive analysis of the ethical and legal implications of adopting AI in judicial proceedings. In particular, it explores the benefits and risks of AI-based techniques in the Italian criminal context, emphasizing the need for their implementation to uphold fundamental legal principles such as transparency, accountability, and human-centered decision-making.

***Index Terms***—Predictive digital techniques, Ethical and legal challenges, Judicial decision-making, Transparency and accountability.

# I. INTRODUCTION AND MOTIVATION

In recent years, the debate on the use of digital technologies in judicial systems has intensified, particularly concerning their potential to transform how legal processes are managed and analyzed [28], [29]. In several European countries, such as France [37], Germany [10], and Estonia [15], advanced tools like predictive algorithms, digital platforms for case management, and natural language processing systems are beginning to support judges and legal professionals in case evaluation. These tools promise to enhance the efficiency of complex tasks, reduce analysis time, and standardize evaluation criteria, ensuring greater access to justice and reducing arbitrariness in decisions [2].

This scenario has prompted the European Union to intervene by defining a regulatory framework to govern the use of Artificial Intelligence (AI) in the legal domain, ensuring that new technologies adhere to fundamental legal and ethical principles. In 2018, the EU adopted an Ethical Charter on AI, establishing guiding values for the development and use of intelligent systems, with a focus on transparency, accountability, and security [12]. This commitment was further reinforced by the AI Act, the world's first law specifically designed to regulate AI, which came into force in June 2024 [36]. The

AI Act aims to ensure that AI is used safely, transparently, and in alignment with fundamental rights, introducing a risk-based classification of AI systems and specific obligations for providers and users.

In Italy, the integration of such technologies is still in its infancy, and the use of advanced systems to support judicial decision-making remains more of an aspiration than a reality [5]. The prospect of introducing digital tools raises questions not only about their technical feasibility but also about the ethical and legal implications that may arise [11], [35]. Italy has adopted a law called the National Digital Strategy 2024-2026, which envisions the development and adoption of advanced digital technologies, including AI in the judicial sector [1]. This strategy aims to promote administrative efficiency and foster the integration of innovative tools while ensuring respect for fundamental rights and privacy protection. However, despite these initiatives providing a regulatory and strategic framework, the practical implementation of AI techniques in the Italian criminal justice system requires careful consideration of ethical and legal implications [11].

Methodologies that adopt digital and AI-based techniques in the legal domain represent an intriguing approach to automating the analysis of complex legal factors. They offer a rigorous model to support judicial decision-making. Referring to the criminal justice field, such methodologies enable the digital representation of key elements of legal reasoning in the analysis of a crime (e.g., the type of violation committed, the dynamics of the event, weather conditions—key aspects in assessing criminal liability in complex cases such as road homicides) [20], [21]. Although Italy has yet to employ similar digital technologies in the criminal context, their adoption could open new opportunities to enhance the system's efficiency and objectivity, provided an in-depth assessment of the ethical risks and legal implications is conducted [30], [35].

The support provided by digital techniques in such decision-making activities poses significant challenges due to the involvement of fundamental principles of the Italian legal system, such as judicial autonomy, decision transparency, and the right to defense. Therefore, critical questions arise, which we will address in this work: i) What are the limits of a digital system in supporting the criminal process? ii) How can we ensure that these tools do not replace but assist judges, respecting their role? iii) How can we frame legal responsibility in cases of errors or incorrect predictions by
the system? This article aims to explore the ethical and legal implications of predictive systems based on digital and AI techniques to support criminal judges in crime analysis. Although not yet integrated into Italian courts, these systems provide an interesting starting point for analyzing the potential benefits and risks of their use. The study will focus on three main dimensions:

1) Decision-making transparency, with particular attention to the comprehensibility of decisions for judges and involved parties;

2) Judicial autonomy, analyzing how these systems might impact discretion and the human role in case evaluation;

3) Legal responsibility, addressing issues related to the accountability of developers, judges, and judicial institutions in using such technologies.

Through a critical analysis, this article intends to contribute to the debate on the ethical and responsible introduction of digital technologies in the Italian criminal justice system. It will also provide recommendations to ensure that technological innovation respects fundamental legal principles, promoting a balance between modernization and the protection of parties' rights. The remaining part of the manuscript is organized as follows: in Section II, we review related work on the application of AI and predictive digital technologies in the legal domain; in Section III, we explore the ethical and legal challenges associated with the integration of AI in justice. Section IV focuses on the specific context of the Italian criminal justice system, using vehicular homicides as a case study to analyze the advantages and challenges of adopting predictive AI tools; Section V concludes the paper.

## II. DIGITAL TECHNOLOGIES IN THE LEGAL DOMAIN

Innovative techniques based on AI are emerging to optimize decision-making processes, demonstrating the broad versatility of AI in this field. For example, the study presented in [22] proposes a predictive digital model to estimate the risk of recidivism among inmates, while in [27], an AI tool adopted by the Swiss police is described for identifying areas with a higher likelihood of criminal activity.

The JudicialTech project [23] aims to make the judicial system more accessible and equitable. Our approach aligns with this vision, utilizing AI to support legal decision-making. On the other hand, the work in [16] uses data from criminal cases handled by the Delhi District Court to classify defendants as acquitted or convicted. The authors applied various machine learning (ML) algorithms, achieving accuracies between 85% and 92% and F1 scores ranging from 86% to 92%. In [4], the authors developed an application of the Support Vector Machine (SVM) algorithm for classifying, clustering, and analyzing documents, improving the efficiency of criminal case management by law enforcement agencies. The results showed accuracies ranging from 73.91% to 89.49%, depending on the datasets analyzed [39]. Similarly, Luo et al. examined various neural networks and SVM implementations to predict criminal charges, achieving accuracy and F1 scores of 98.97% and 98.51%, respectively, though with minimum

values of 42.9% and 41.16%. In the United Kingdom, the study presented in [17] introduces a digital system designed to predict legal decisions or sentences based on English-language documents and supervised learning algorithms. The results reported accuracy rates ranging from 49.6% to 69.1%. In a similar approach, [25] adopted the Camembert algorithm, a transformer-based model, to anticipate the outcomes of legal proceedings, achieving an average accuracy of 84.1% and an F1 score of 89.55%. An extensive study of models applied to Natural Language Processing (NLP) in the legal sector was conducted in [18]. The authors examined eight legal datasets of varying sizes (ranging from 900 to 57,000 records) used for tasks such as binary and multilabel classification, multiple-choice questions, summarization, and information retrieval. In parallel, [34] developed a virtual assistant based on a modified version of the GPT-3 model, specifically trained for the legal context; this tool assists users in creating legal documents and providing advice. In [26], a methodology is proposed for specializing large language models (LLMs) in specific fields, such as law. Lawyer LLaMA, an LLM focused on legal applications, was developed and enriched with legal knowledge through targeted fine-tuning and the addition of a retrieval module to select relevant articles before generating responses. Similarly, in [32], the authors created LexGPT, a language model based on the GPT-J architecture, further optimized with human feedback and professional legal data. The study in [20] illustrates a predictive approach to determining the presence of vehicular homicide, leveraging AI techniques to extract relevant features from the crime scene. Based on Boolean logic, this system anticipates legal responses with a precision of 83.2%. An evolution of this work is presented in [21]: the first hardware circuit replicating judicial reasoning, achieving an accuracy of 86%. Finally, in [31], a neural network was designed to assess the risk of life-threatening situations in stalking cases, achieving 80% accuracy. Table I summarizes all the previously cited works.

## III. ETHICS AND LAW IMPACTS IN AI-BASED TECHNIQUES IMPLEMENTED IN JUSTICE

Despite significant technical advancements, several studies have highlighted the risks and challenges associated with adopting AI-based technologies in legal contexts, emphasizing the need to address such systems' ethical and legal implications. For example, the issue of algorithmic bias has been extensively discussed in [19], where it is analyzed how errors or distortions in training data can influence legal decisions in ways that compromise the fairness and neutrality of judicial processes. The use of unbalanced historical data or data contaminated by pre-existing biases risks perpetuating systemic discrimination, raising important ethical concerns related to algorithmic justice.

Additionally, [7], [9] explores the broader implications related to the loss of judicial autonomy, which can occur when AI-based tools take on a predominant role in decision-making. This scenario could lead to an excessive delegation of legal responsibilities to machines, altering the traditional relation-
<table><caption>TABLE I
OVERVIEW OF VARIOUS STUDIES ON THE TOPIC</caption><thead><tr><th>References</th><th>Content</th><th>Best acc.</th><th>F1</th><th>AUC</th></tr></thead><tbody><tr><td>[22]</td><td>Proposes a digital predictive model to estimate the risk of recidivism in prisoners</td><td>81%</td><td>N/A</td><td>74%</td></tr><tr><td>[27]</td><td>AI tool adopted by the Swiss police to identify areas with higher likelihood of criminal activity</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>[16]</td><td>Data related to criminal cases to classify defendants as acquitted or convicted</td><td>92%</td><td>92%</td><td>N/A</td></tr><tr><td>[39]</td><td>Application of SVM to classify, cluster, and analyze documents</td><td>89.49%</td><td>N/A</td><td>N/A</td></tr><tr><td>[4]</td><td>Neural networks and SVM to predict criminal charges</td><td>98.97%</td><td>98.51%</td><td>N/A</td></tr><tr><td>[17]</td><td>Digital system to predict legal decisions or judgments</td><td>69.1%</td><td>N/A</td><td>N/A</td></tr><tr><td>[25]</td><td>Use of Camembert to anticipate the outcome of legal proceedings</td><td>84.1%</td><td>89.55%</td><td>N/A</td></tr><tr><td>[18]</td><td>Legal datasets for classification and information retrieval</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>[34]</td><td>Virtual assistant based on GPT-3 for the legal context</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>[26]</td><td>Lawyer LLaMA, an LLM focused on legal applications</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>[32]</td><td>LexGPT, based on GPT-J and optimized for the legal sector</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>[31]</td><td>Neural network to assess the risk in stalking cases</td><td>80%</td><td>N/A</td><td>N/A</td></tr><tr><td>[20]</td><td>Prediction of minimal characteristics for road homicide</td><td>83.2%</td><td>88%</td><td>79%</td></tr><tr><td>[21]</td><td>Hardware emulation to evaluate road homicide</td><td>86%</td><td>N/A</td><td>N/A</td></tr><tr><td>[19]</td><td>Analysis of errors in training data and their impact</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>[7], [9]</td><td>Implications of loss of judicial decision-making autonomy</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>[30], [33]</td><td>Responsibility in case of AI errors or malfunction</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>[8]</td><td>Transparency and explainability of AI decisions</td><td>N/A</td><td>N/A</td><td>N/A</td></tr></tbody></table>

ship between the judge and the judicial system. Another risk is the dehumanization of the decision-making process, where the judge might rely on automated decisions that lack the sensitivity necessary to understand the nuances and human context of a specific case.

Another area of growing interest is the challenge of legal accountability, addressed in [30], [33], where the complexity of establishing who is responsible in case of an AI system error or malfunction is emphasized. Fundamental issues also pertain to the transparency and explainability of AI-supported decisions. As highlighted in [8], a judicial system must ensure that every decision is understandable and justifiable for both the involved parties and the public. The lack of explainability in AI models, especially those based on deep neural networks, represents a significant barrier to their acceptance in legal contexts, where transparency is a critical requirement for maintaining trust in the system.

In summary, while AI technologies offer significant opportunities to improve the efficiency and precision of legal decision-making, their adoption requires a careful assessment of ethical and legal implications. These works underline the need for a balanced approach that combines technological innovation with a strong commitment to ensuring fairness, judicial autonomy, transparency, and accountability within the judicial system.

## IV. THE ANALYSIS OF ETHICAL AND LEGAL CHALLENGES IN THE ITALIAN CRIMINAL JUSTICE

To analyze and discuss ethical and legal challenges and issues in Italian criminal Justice, we considered vehicular homicides as a case study. Predictive AI techniques are easier to apply as support for judicial decision-making in assessing a road homicide than other crimes, as the former primarily relies on objective and verifiable elements. This type of crime, indeed, generally involves a clear and documentable violation of traffic laws, such as speeding, drunk driving, or failure to yield.

### A. Advantages in Ethical and Legal Implications

One of the main contributions that AI can offer to the legal sector lies in its ability to improve the operational efficiency of the judicial system by optimizing case management times and streamlining available resources [6]. These benefits are particularly relevant in areas such as vehicular homicide, a type of crime that requires a complex reconstruction of events, often based on heterogeneous evidence, such as witness statements, technical reports, and data collected at the scene of the incident. In the Italian context, characterized by chronic delays and inefficiencies in the judicial system [44], the application of AI-based solutions can represent a significant step toward greater speed and fairness in handling such cases.

The challenges related to managing complex proceedings, such as those for vehicular homicide, exacerbated by a significant backlog of cases in recent years, have driven the Italian government to introduce structural reforms, many of which are supported by the National Recovery and Resilience Plan (PNRR) [38]. In this context, advanced automation tools, such as classification algorithms and predictive analysis, can facilitate repetitive and analytically intensive tasks, allowing judges to focus on the most critical decision-making aspects. For instance, AI can expedite the reconstruction of events related to a fatal traffic accident by quickly analyzing complex data, such as vehicle positioning, road conditions, technical report findings, and vehicle safety system recordings (e.g., "black boxes"). This process, which traditionally requires extended periods, can be significantly streamlined thanks to AI's ability to detect patterns and correlations within data in a very short time.

Moreover, AI can support the identification of essential elements for the final decision, reducing the risk of errors and increasing procedural fairness [20], [21]. For example, in cases of vehicular homicide, automated evidence analysis could help determine the degree of responsibility of the individuals involved with greater precision, taking into account variables such as vehicle speed, potential traffic code violations, and the psychophysical conditions of the drivers. This approach would not only improve the quality of judicial decisions but also reduce the costs and time required to conclude proceedings, ensuring greater accessibility and sustainability of the judicial system for citizens. It is essential to emphasize that, especially in sensitive areas such as criminal justice and particularly in proceedings for vehicular homicide, not all activities can be automated. Decisions that profoundly impact human rights and fundamental freedoms inevitably require human intervention and the emotional and evaluative dimension that only judges can provide [13], [42].

Another positive aspect of integrating AI in the legal domain could be the reinforcement of uniformity in judicial decisions, thereby mitigating interpretative discrepancies that may arise in analyzing complex cases. The ability of algorithms to process and compare a large corpus of past decisions with the circumstances of current cases promotes greater consistency in applying legal norms, reducing the risk of
<figure><img src="image_2.png" /><figcaption>Fig. 1. Overview of the legal decision-making process supported by AI technologies</figcaption></figure>

interpretative inconsistencies and improving the overall quality of decisions [13], [42]. In the context of vehicular homicides, for instance, adopting predictive algorithms could provide significant support for judges, especially in managing complex cases and evaluating heterogeneous evidentiary elements, such as the degree of culpability of the driver, the nature of traffic law violations, or the existence of aggravating or mitigating circumstances. By analyzing large amounts of data related to similar legal precedents, these algorithms could provide judges with useful insights to identify established interpretative trends and highlight decision-making criteria adopted in analogous past situations. This approach would not only strengthen the coherence and uniformity of decisions but also help define a more robust and standardized normative and interpretative framework for the qualification of the crime [20], [21], [42].

A further consequence of AI's integration is its ability to reduce judicial arbitrariness in decision-making, an aspect that can, in some ways, be considered an advantage. Inconsistent or non-uniform decisions on similar cases represent one of the main causes of distrust in the judicial system. By applying objective criteria based on empirical data, predictive algorithms can significantly reduce these discrepancies, ensuring greater consistency with legal precedents and applicable norms [14], [42]. When connected to the fundamental principles of the Italian criminal justice system, all the above-mentioned advantages contribute to strengthening the principle of legal certainty, which ensures transparency and predictability in the legal consequences of actions [14], [42]. Additionally, this helps ensure the respect of fundamental rights, such as the right to defense [14], [42]. The use of such techniques increases decision predictability, enabling defendants to structure more effective and targeted defense strategies, consequently improving the overall quality of defense [14], [42].

The advantages of AI also extend to the cognitive dimension, introducing significant opportunities to improve judicial decision-making. The intrinsic neutrality of algorithms, if correctly designed and validated, could reduce the risk of subjective biases, emotions, or cognitive distortions that may influence human judgment [24]. For instance, in the case of vehicular homicide, the judge might be influenced by the emotional impact of particularly tragic circumstances or the informational load of complex evidence. A well-trained algorithm, on the other hand, would be able to analyze the

objective data of the case, such as the vehicle's speed, the driver's blood alcohol level, or traffic law violations, providing decision-making support based solely on standardized and emotion-free criteria. This reduces the risk of evaluation errors linked to non-rational factors, ensuring a more transparent, fair, and objective-focused decision-making process. Thanks to their ability to systematically and objectively analyze data, algorithms can overcome some intrinsic limitations of human reasoning, such as anchoring to initial information or reliance on more easily recallable data, known as cognitive biases. This characteristic does not eliminate the importance of human judgment but complements it, providing support that facilitates the evaluation of evidence-based on standardized and transparent criteria. Additionally, AI tools, by identifying patterns and correlations that might escape human observation due to the complexity or volume of information [11], [14], [42], enable judges to make more informed decisions, reducing the risk of errors stemming from incomplete or distorted interpretations.

## B. Critical Ethical and Legal Implications

A central risk is the so-called "judicial conformism," often referred to as the "herd effect". Predictive algorithms rely on historical data to identify decision-making patterns and established jurisprudential trends [14], [42]. However, this retrospective approach risks crystallizing outdated or no longer socially relevant legal interpretations. For instance, a rigid interpretation of a rule defining the essential requirements for classifying vehicular homicide may have been appropriate in the past but could become inadequate in a changed socioeconomic context. The algorithm might continue to suggest the old interpretation, hindering legal innovation [3].

This phenomenon could not only limit the evolution of law but also conflict with the dynamic role of judges, who should adapt their decisions to the specific circumstances of the case and emerging social sensitivities. Moreover, the use of predictive models based on established jurisprudence risks marginalizing minority or innovative legal approaches, which could represent progress for the legal system. The intrinsic inflexibility of algorithms may thus compromise the ability of law to respond to the needs of an evolving society [3].

Another critical limitation is algorithmic bias, which is closely tied to the quality and representativeness of the training datasets. If historical data contain biases, discrimination, or unjust practices, the algorithm will merely replicate them,
amplifying their impact [40]. For example, in the context of road homicides, if the training data predominantly includes harsher penalties for specific groups, such as young drivers or immigrants, the algorithm might perpetuate this pattern. As a result, recommendations could disproportionately penalize these groups, regardless of the case's specific circumstances.

The issue of bias is exacerbated by algorithmic opacity, i.e., the inability to fully understand how an algorithm arrives at a particular decision [14], [40], [42]. This opacity often stems from the complexity of machine learning models. While these systems can process vast amounts of data with high efficiency, operating as "black boxes" makes it challenging to understand the decision-making process behind a given output. The lack of transparency complicates identifying, explaining, and correcting biases, potentially undermining fundamental legal principles such as equality and raising doubts about the impartiality of automated judicial decisions. The lack of explainability can erode trust in the judicial system, particularly when decisions directly impact the rights and freedoms of those involved.

The difficulty in understanding and explaining algorithmic decisions could also significantly limit the ability of parties to fully exercise their right to defense. If the reasoning behind a decision is opaque or incomprehensible, the parties may find themselves unable to challenge or refute the system's outputs. This risk could foster perceptions of injustice and the exclusion of the human factor in the process, directly contradicting the fundamental principles of due process. Moreover, the opacity of algorithms would also present challenges for judges, who, deprived of clear and verifiable explanations, would struggle to adequately justify their decisions to the parties and the public [43]. Such a scenario risks violating the obligation to provide reasoning for judicial decisions, a cornerstone of the Italian judicial system.

Entrusting decision-making processes to an algorithm also raises serious questions about legal accountability. In judicial decisions, the principle of personal responsibility [30] requires that the judge be fully accountable for their decisions. However, the introduction of predictive systems could lead to the judge passively accepting the algorithm's suggestions without a thorough understanding of its analyses. This risk of decision-making automation compromises not only the quality of judgments but also trust in their legitimacy. There is growing concern that these algorithms could undermine judicial discretion, fostering technological dependency that diminishes the importance of human intervention [41].

Moreover, technical errors, such as flawed programming or poor-quality datasets, can lead to false correlations or erroneous conclusions. The judge, traditionally the guarantor of the delicate balance between legal norms and the human context of specific cases, should never become a mere validator of decisions suggested by the system, losing the ability to exercise critical, discretionary, and, when necessary, corrective judgment. Such dynamics risk not only excessive standardization of decisions—which, while promoting greater consistency in similar cases, might overlook the unique complexities of

each situation—but also amplifying the risk of judicial errors in cases of system malfunctions or incorrect assessments [42].

Given these considerations, who should be held accountable for injustices becomes complex: the programmer, the software provider, or the judge who adopted the algorithm-based decision? One of the most critical aspects concerns the loss of the human dimension in criminal decision-making. Human judgment is not limited to the mechanical application of norms but integrates an ethical, social, and value-based evaluation that accounts for the specificities of the case. Algorithms, however sophisticated, cannot fully comprehend the moral nuances or the emotional and social implications of a decision [43].

For example, in a vehicular homicide case, the judge must not only assess the facts and apply the law but also consider the social context, the consequences for victims, and the moral responsibility of the defendant. A purely algorithmic approach risks reducing these complex evaluations to mere statistical applications devoid of sensitivity to the human specificities of the case. This deficiency can lead to decisions perceived as cold and dehumanizing, undermining citizens' trust in the judicial system.

Ultimately, the use of AI could be perceived as an excessive delegation to technology at the expense of human judgment, which remains the cornerstone of criminal proceedings. This perception could further erode citizens' trust in institutions, particularly in cultural contexts where automation is viewed with suspicion. It is, therefore, essential to integrate AI transparently and with a clear supporting role rather than as a substitute for human judgment.

## V. CONCLUSION

The integration of predictive technologies into the Italian criminal justice system presents a unique opportunity to enhance efficiency, standardize decision-making, and reduce case processing times. However, these advancements must be carefully balanced against significant ethical and legal challenges to ensure that AI adoption in judicial processes upholds fundamental principles of fairness, transparency, and accountability.

While predictive AI offers clear advantages, such as improved consistency in judicial decisions, resource optimization, and reduced biases stemming from emotional or cognitive distortions, it also poses substantial risks. Algorithmic bias, for example, can perpetuate systemic discrimination if training datasets are not carefully curated to ensure equitable treatment. The opacity of decision-making processes in many AI systems complicates efforts to ensure that decisions are comprehensible and justifiable, particularly when these decisions have profound impacts on individuals' rights and freedoms. Furthermore, excessive reliance on AI risks undermining judicial autonomy by reducing the critical role of human oversight, while also raising complex questions of accountability when errors or malfunctions occur. To address these issues, AI systems must be designed with safeguards that include transparent and interpretable outputs, legal frameworks to define
<table><caption>TABLE II
HOW TO MITIGATE ISSUES IN PREDICTIVE DIGITAL TECHNIQUES</caption><thead><tr><th>Issues</th><th>Proposed Strategy</th></tr></thead><tbody><tr><td>Algorithmic Bias</td><td><ul><li>Balanced datasets to ensure all categories</li><li>Debiasing algorithms during training</li><li>Ensure diversity in data used for training</li></ul></td></tr><tr><td>Algorithmic Opacity</td><td><ul><li>Adopt interpretable machine learning models</li><li>Decision-making processes explainable techniques</li><li>Clear documentation on algorithm functionality</li></ul></td></tr><tr><td>Loss of Judicial Decision-Making Autonomy</td><td><ul><li>Algorithms for supporting (not replacing) judges</li><li>Guidelines on the use of predictive tools</li><li>Training for judges on the use of AI</li></ul></td></tr><tr><td>Legal Liability Risks</td><td><ul><li>Clear responsibility for developers, providers, users</li><li>Ensure traceability of algorithmic decisions</li><li>Review mechanisms for decisions made with AI</li></ul></td></tr><tr><td>Legal Crystallization</td><td><ul><li>Continuously monitor of the legal interpretations</li><li>Integrate innovative legal approaches into datasets</li><li>Regularly update algorithms for new sensitivities</li></ul></td></tr></tbody></table>

accountability, and mechanisms for continuous updates to reflect evolving legal and social contexts.

In conclusion, while AI holds the potential to revolutionize the Italian criminal justice system, its adoption must be grounded in ethical considerations and robust safeguards to preserve the integrity of judicial processes. A multidisciplinary approach, bringing together technologists, legal experts, and ethicists, is essential to ensuring that AI serves as a complement to human judgment rather than a substitute. Through careful and thoughtful integration, predictive technologies can enhance the fairness, efficiency, and trustworthiness of the judicial system, ensuring that innovation aligns with the principles of justice and societal trust.

## REFERENCES

[1] Strategia Italiana per l'Intelligenza Artificiale 2024-2026, 2024.

[2] M. Monaca A. Busacca. Using ai for justice: Principles and criteria of the "european ethical charter on the use of ai in judicial systems". In *Artificial Intelligence and Economics: the Key to the Future*. 2022.

[3] G. Ariolli. Nomofilachia, giustizia predittiva e intelligenza artificiale. *Diritto e Innovazione*, Nov 2023.

[4] Bingfeng et al. B. Luo. Learning to predict charges for criminal cases with legal basis. 2017.

[5] C. Barbaro. Uso dell'intelligenza artificiale nei sistemi giudiziari: verso la definizione di principi etici condivisi a livello europeo. *Questione giustizia*, 2018.

[6] C. Bernardo. Intelligenza artificiale, utilità e insidie nell'uso giurisdizionale. *La Magistratura*, 2024.

[7] F. C. Gastaldo. Il giudice-robot: l'intelligenza artificiale nei sistemi giudiziari tra aspettative ed equivoci. *Ius In Itinere*, 2021.

[8] E. Carloni. La trasparenza amministrativa e gli algoritmi. *Intelligenza artificiale per la pubblica amministrazione*, 2023.

[9] CEPEJ. Guidelines on how to drive change towards cyberjustice, 2016.

[10] European Commission. e-codex: Facilitating digital communication in justice. https://ec.europa.eu/info/e-code-x, 2024.

[11] Rosa Corrado. Intelligenza artificiale e processo: tra diritto ed etica. *Iura Legal Systems*, XI(2, B (3)):43-57, 2024.

[12] Council of Europe. European Commission for the Efficiency of Justice (CEPEJ). European Ethical Charter on the Use of Artificial Intelligence in Judicial Systems and their environment, 2018.

[13] A. D'Aloia. AI Law, come cambiano le sentenze con gli algoritmi: lo scenario. *Agenda Digitale*, 2024.

[14] N. Durante. Brevi considerazioni in tema di amministrazione algoritmica e di giustizia predittiva. In *Convegno di studi: Algoritmi, identità digitale e personalità umana: decisione, giustizia ed etica nell'intelligenza artificiale*, Sarno (SA), Italia, 2024. Società Italiana Avvocati Amministrativisti, Sezione di Salerno ed Avellino (S.A.S.A.).

[15] e Estonia. e-toimik: The digital court file system. https://e-estonia.com/solutions/e-governance/e-judiciary/, 2024.

[16] R. A. Shaikh ee al. Predicting outcomes of legal cases based on legal factors using classifiers. *Procedia Computer Science*, 167:2393-2402, 2020.

[17] B. Strickson et al. Legal judgement prediction for uk courts. In *Proceedings of the 3rd International Conference on Information Science and Systems*, pages 204-209, 2020.

[18] D. Song et al. On the effectiveness of pre-trained language models for legal natural language processing: An empirical study. *IEEE Access*, 10:75835-75858, 2022.

[19] E. Ntoutsi et al. Bias in data-driven ai systems – an introductory survey. *arXiv preprint arXiv:2001.09762*, 2020.

[20] G. Garzo et al. Opening the black box: How boolean ai can support legal analysis. In 2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI), pages 269-272, 2024.

[21] G. Garzo et al. The first hardware circuit emulating italian road homicides legal logic, daje! In *Proceedings of the IADIS International Conference on e-Society*, Lisbon, Portugal, March 2025. IADIS.

[22] G.V. Travaini et al. Machine learning and criminal justice: A systematic review of advanced methodology for recidivism risk prediction. *International journal of environmental research and public health*, 19(17):10594, 2022.

[23] J. Barnett et al. Judicialtech supporting justice. 2023.

[24] L. Fabbri et al. Le professioni giuridiche tra razionalità tecnica e razionalità riflessiva: il caso della magistratura. *PROFESSIONALITÀ STUDI*, 2:18-38, 2022.

[25] L. Martin et al. Camembert: a tasty french language model. *arXiv preprint arXiv:1911.03894*, 2019.

[26] Q. Huang et al. Lawyer llama technical report. *arXiv preprint arXiv:2305.15062*, 2023.

[27] S. Monika et al. Smart criminal justice: Phenomena and normative requirements. *International Review of Administrative Sciences*, 89(2):415-432, 2023.

[28] F. Galli and G. Sartor. Ai approaches to predictive justice: A critical assessment. *Humanities and Rights Global Network Journal*, 2023.

[29] Grazia Garzo and Alessandro Palumbo. Human-in-the-loop: Legal knowledge formalization in attempt controlled english. In 2025 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (ISDFS), Boston, MA, USA, 2025. IEEE, IEEE.

[30] C. Guittton et al. Responsible automatically processable regulation. *AI & Society*, 2024.

[31] S. Icardi. Predicting the Threat of Death in Stalking Cases Through Artificial Neural Network. Ontario, Canada, 2020.

[32] J. Lee. Developing lexgpt models: Enhancing gpt-j with legal data from pile of law. *Journal of Legal Technology*, 2023.

[33] G. Fontana N. Barbuzzi. La responsabilità da trattamento algoritmico dei dati alla luce della normativa europea e della giurisprudenza comunitaria e italiana. *Rivista di Digital Politics*.

[34] T. Nguyen. A virtual legal assistant based on gpt-3 for interactive legal support. *Journal of Artificial Intelligence Research*, 2023.

[35] Laura Palazzani. Tecnologie dell'informazione e intelligenza artificiale: Sfide etiche al diritto. Edizioni Studium Srl, 2020.

[36] Parlamento europeo e Consiglio dell'Unione europea. Regolamento (UE) 2024/1689, 2024.

[37] Predictice. Predictice: Predictive analytics for legal cases, 2024.

[38] Presidenza del Consiglio dei Ministri. Piano Nazionale di Ripresa e Resilienza (PNRR), 2021.

[39] T. P. Hsing S. Chou. Text mining technique for chinese written judgment of criminal case. In *Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2010*, 2010.

[40] G. Sartor. L'intelligenza artificiale e il diritto. Il Mulino, Nov 2022.

[41] Nicola Scerbo. Temere l'intelligenza artificiale? Il ruolo del giudice e l'insostituibilità della funzione autonoma di giudizio nel sistema penale. *Giurisprudenza Penale*, 2023.

[42] E. Tagliasacchi. Il sindacato del giudice sull'attività amministrativa algoritmica tra etica benthamiana e 'black box': il futuro ha un cuore antico? *Giustizia Amministrativa*, 2024.

[43] G. Ubertis. Intelligenza artificiale, giustizia penale, controllo umano significativo. *Sistema Penale*, Nov 2020.

[44] F. Viapiana. Le inefficienze e i costi della giustizia italiana. *Rivista di Politica Economica*, 2017.