# The First Hardware Circuit Emulating Italian Road Homicides Legal Logic, DAJE!

Grazia Garzo, Alessandro Palumbo

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Grazia Garzo, Alessandro Palumbo. The First Hardware Circuit Emulating Italian Road Homicides Legal Logic, DAJE!. IS 2025 - 18th IADIS International Conference Information Systems, Mar 2025, Madeira island, Portugal. pp.1-8. [hal-04990194](https://hal.science/hal-04990194v1)

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# THE FIRST HARDWARE CIRCUIT EMULATING ITALIAN ROAD HOMICIDES LEGAL LOGIC, DAJE!

Grazia Garzo¹ and Alessandro Palumbo²

¹University of Siena, Italy

²CentraleSupelec, Inria, Univ Rennes, CNRS, IRISA, France

## ABSTRACT

The legal field has increasingly adopted digital technologies in recent years, enhancing efficiency and speed in expert tasks. The integration of these technologies significantly streamlines investigative and decision-making processes, reducing the time and costs traditionally associated with legal procedures. However, this increase in digital system usage necessitates stringent security measures, given that these systems often handle sensitive data requiring strict confidentiality. It is, therefore, crucial to implement safeguards against system tampering and to ensure the privacy of processed information, thereby protecting sensitive data from potential breaches. In this con- text, implementing software functionalities directly on hardware emerges as a promising solution, reducing the exposure to security vulnerabilities typically associated with software. Consequently, we propose the Digital Assurance Judicial Enforcer (DAJE), which, to the best of our knowledge, is the first Boolean-based hardware circuit designed to implement and emulate Italian legal logic for road homicides. DAJE implements 6 LUTs and 24 FFs. Its maximum operating frequency is 642 MHz, with an average power consumption of 89 mW on an FPGA emulation.

## KEYWORDS

Boolean Function, Road Homicide, Italian Legal System, Judicial Systems

## 1. INTRODUCTION

The legal sector has significantly transformed in recent years due to the increasing integration of digital technologies. These cutting-edge tools can potentially revolutionize legal experts' operational methods, enhancing efficiency and expediting decision-making processes (American Bar Association, 2021; Deloitte, 2020). The digitalization of processes leads to substantial savings by reducing the need for physical resources and optimizing the work of judicial personnel, thereby freeing up time for more critical activities. Adopting advanced technologies such as artificial intelligence (AI) and machine learning allows for the automation of numerous repetitive and complex tasks, reducing analysis times and increasing the precision of legal decisions (J. Armour et al, 2022; Legal Futures, 2024). For instance, implementing sophisticated algorithms for legal data analysis can facilitate the search for relevant legal precedents, improve legal risk assessment, and more effectively support investigations (Legal Futures, 2024). Various software solutions employed in the field of justice can be cited. For example, online portals and court management platforms facilitate access to documents and rulings, enhancing the transparency of the judicial system. Using artificial intelligence and advanced algorithms enables the analysis of large volumes of legal data to predict case outcomes and support judges in their decisions. Furthermore, court management software organizes hearings efficiently and monitors the status of cases in real-time, improving the overall management of the court's workload.

Recent technological developments have focused largely on integrating software functionalities into hardware (i.e., ASICs and FPGAs) to boost system performance and reduce power consumption (J. Kim et al, 2023). Generally, a software program executed on a CPU or GPU tends to consume more power than the same function implemented on an FPGA. This is because CPUs and GPUs are designed to be generic and versatile hardware. In contrast, FPGAs can be configured to perform specific operations in a highly parallel manner, with logic optimized for a particular function, thereby reducing the number of unnecessary operations and energy consumption (F. Pacini et al, 2024; M. Qasaimeh et al, 2019). To give some examples, M.S. Brunella et al, 2022 and S. Pontarelli et al, 2019, respectively, networking functions and Linux kernel components have
been directly ported to FPGAs. Similarly, K. Arıkan et al, 2022; A. Palumbo et al, 2021; 2023a2023b, microprocessor security functions have been offloaded to dedicated hardware modules. One of the main advantages of hardware-based security is physical isolation, which the software could not provide. Hardware can physically separate sensitive operations from the rest of the system, reducing exposure to common software vulnerabilities such as buffer overflows, malware, or privilege escalation attacks. This isolation reduces the attack surface, making it harder for an adversary to compromise critical security operations. Another key strength of hardware is its tamper resistance: specific hardware designs include mechanisms that can detect and respond to physical tampering, such as erasing sensitive data or shutting down the system if tampering is detected (L. Cassano et al, 2022; P. R, Nikiema et al, 2023. Additionally, secure hardware enclaves (Intel, 2016; ARM 2023) ensure that sensitive operations, such as key generation, occur in a protected environment that software-based attacks can't easily reach (J. Menetrey, 2022).

<figure><img src="image_3.png" /><figcaption>Figure 1. The overview of the proposed approach</figcaption></figure>

Guaranteeing that systems are highly secure with the increasing adoption of digital technologies in the legal domain is critical. Data handled in the legal field is often extremely sensitive, including personal information, judicial evidence, and other confidential data that, if disclosed, could have serious legal and reputational consequences (Deloitte, 2020; GOV.UK, 2022). Consequently, it is essential to implement robust security measures to protect these systems from tampering attempts and ensure the privacy of information as required by privacy regulations such as the General Data Protection Regulation (GDPR) (European Parliament, 2016). Protecting sensitive data requires adopting technologies capable of preventing unauthorized access, protecting against cyber-attacks, and ensuring data integrity throughout its lifecycle (American Bar Association, 2021; GOV.UK, 2022). In this context, functions implemented directly in hardware represent a promising solution to improve the security of software adopted in digital legal systems, offering additional protection due to its physical nature (J. Armour, 2022 GOV.UK, 2022). In addition, if an attacker manages to inject something referred to as malicious software, he/she could potentially compromise and disable the software defenses protecting the system. In contrast, an attacker would likely need physical access to the microprocessor where the hardware circuitry is integrated to sabotage it, making it a more resilient security measure against certain types of attacks. These advantages make hardware solutions particularly suitable for protecting critical information in legal systems while ensuring high performance and reliability (American Bar Association, 2021; Legal Futures, 2024). Given this reality, there is an urgent need to explore preemptively hardware-based methodologies for legal evaluations akin to the approaches already implemented in other domains. Many software functions have been offloaded to dedicated hardware modules to mitigate vulnerabilities. Techniques like DAJE are pioneering examples of how Boolean logic and hardware integration can emulate legal reasoning while offering physical security, tamper resistance, and consistent performance. Judicial systems often experience significant backlogs, resulting in lengthy case resolutions. DAJE-based methodologies can alleviate this burden by automating preliminary analyses and rule-based decision-making, expediting case management while adhering to the transparency and traceability requirements for algorithms mandated by the proposed AI Act (European Commission, 2024).

This manuscript provides a foundation for further exploration of how digital technologies and hardware implementations can be integrated into the legal sector to improve judicial processes' security, efficiency, and reliability, which remain challenging (Legal Futures, 2024; GOV.UK, 2022). To the best of our knowledge, we propose the first hardware-based legal function evaluating crime sciences. The workflow of the presented approach is shown in Figure 1. This paper aims to introduce a novel idea that bridges hardware and legal systems. We are aware that FPGAs are devices well-known for their capability to implement complex functions due to their programmable architecture. The implementation of DAJE on an FPGA is mentioned for demonstrative purposes, serving as a practical example of how these technologies can be utilized to emulate legal logic in judicial applications. Functions implementation on FPGAs lies not only in their superior
resilience, reliability, and security compared to software solutions but also in their reprogrammable nature. This adaptability allows the hardware to be updated as new requirements emerge. For instance, the conditions for detecting specific crimes or additional features to monitor can be seamlessly modified or extended through reprogramming. This capability ensures that the system remains future-proof, allowing it to adapt to evolving legal frameworks. In addition, our aim is not to replace human judgment or manage the investigative phase directly. Instead, we seek to lay the foundation for demonstrating to the research community that legal reasoning can be implemented in hardware. This approach offers a resilient methodology to support the legal phase, where the judges evaluate whether the necessary conditions for a crime are met.

The rest of the manuscript is organized as follows: Section 2 examines the works related to our proposal, Section 3 details the proposed methodology, reporting the results and the hardware implementation details. Section 4 concludes the paper.

## 2. RELATED WORK

Various AI-driven techniques have emerged to facilitate decision-making processes, demonstrating the adaptability of AI. For instance, G. V. Traviani et al, 2022 presents a digital predictive model for assessing the probability of recidivism among convicts, while M. Simmler et al, 2023, an AI-powered tool is used to assist Swiss law enforcement in predicting crime-prone areas.

JudicialTech (J. Barnett et al, 2023) aims to improve access to justice and potentially enhance fairness within the judicial system. Our proposed approach is similar, as we leverage AI for legal decision-making. On the other hand, R. A. Shakh et al, 2020 categorize individuals as acquitted or convicted based on data from criminal cases in the Delhi District Court concerning murder incidents. They use various ML models, achieving accuracies ranging from 85% to 92% and F1 scores between 86% and 92%.

While our study involves a similar number of cases, our methodology differs in terms of feature extraction and decision-making processes. Meanwhile, Chou et al. propose using the Support Vector Machine (SVM) algorithm for document classification, clustering and search to enhance the efficiency of law enforcement departments in managing written criminal judgments. They achieve accuracies ranging from 73.91% to 89.49%, depending on the datasets (S. Chou et al, 2010). Similarly, B. Luo et al, 2017 evaluates different neural networks and SVM implementations to predict criminal charges, achieving high accuracy scores and F1 values, reaching up to 98.97% and 98.51%, respectively, with lower values at 42.9% and 41.16%.

B. Strickson et al, 2020 proposes a digital system for predicting legal decisions or judgments. They leverage English documents and supervised learning algorithms, achieving accuracy rates between 49.6% and 69.1%. A similar approach is described in L. Martin et al, 2019, where the authors use a transformer-based judicial algorithm, Camembert, to forecast judicial decisions, with an average accuracy of 84.1% and an F1 score of 89.55%.

A detailed empirical study on models designed for legal Natural Language Processing (NLP) tasks to assess the performance of pre-trained language models in the legal context is conducted by D. Song et al, 2022. The reviewed approximately eight distinct legal datasets, whose sizes varied from 900 to 57,000 records. These datasets encompassed five NLP tasks: binary classification, multilabel classification, multiple-choice QA, summarization, and information retrieval. T. Nguyen, 2023 developed a virtual legal assistant based on a modified version of the GPT-3 model, fine-tuned for the legal domain. This assistant was programmed to interactively assist users by answering legal questions, creating legal documents, and providing legal advice.

Q. Huang et al, 2023 present a new method for adapting large language models (LLMs) to specific sectors is proposed, resulting in the development of Lawyer LLaMA, an LLM dedicated to legal applications. During its initial training phase, continuous integration of legal knowledge was prioritized, along with training on legal professional skills through meticulously managed fine-tuning tasks. To reduce errors in content generation, they also added a retrieval module that sources relevant legal articles before the model addresses any queries.

J. Lee, 2023 engineered a legal-specific language model, elaborating on the development of LexGPT models based on the GPT-J architecture and pre-trained using the Pile of Law. This model underwent further enhancement through methods based on human feedback and professional data tuning, ensuring that modifications to the model's code were unnecessary.

S. Icardi, 2020 developed a Neural Network to predict the risk of life threats in stalking cases is reported, reaching an accuracy of 80%.
The idea for our proposed work was inspired by G. Garzo et al, 2024, where the authors proposed a boolean function to perform legal responses. This manuscript presents the hardware implementation of a similar function. In summary, our research contributes to the expanding body of AI-supported decision-making in the legal realm, offering a unique approach that emphasizes explainability and transparency. This aligns with ongoing advancements in AI and ML (G. Conti, 2020; J. Cui et al, 2023), enriching the landscape of decision-making support in legal contexts. These studies represent an important knowledge base for developing advanced and secure solutions for logical processing. They could have significant applications in the legal field, where a high level of security and privacy in data handling is required, highlighting the potential of advanced digital circuits in enhancing technologically advanced legal infrastructures.

# 3. METHODOLOGY

Legal, logical reasoning is based on the presence and absence of elements (R. Giovagnoli, 2021). For this reason, we thought a Boolean could be close to emulating it. In the initial phase of our estimation, we identified the most pertinent features describing road homicide scenarios according to the Italian law (G. Musco et al, 2013; R. Giovagnoli, 2023; P. M. Sabella, 2022). Then, we emulated the legal, logical connections using Boolean functions by implementing our Digital Assurance Judicial Enforcer (DAJE) circuit. In the final phase, we tested its accuracy by comparing its responses to judges' verdicts on real road homicide cases that happened in Italy. The test dataset utilized in this research comprises data from 100 incidents of road homicide recorded in Italy. Each incident is represented by a textual description detailing 25 elements characterizing the scene of the crime (such as the type of the victim/defendant violation, if they were driving with a valid driving license, and so on). These elements were delineated in accordance with Italian legislation concerning road homicide incidents (P. M. Sabella, 2022) and are reported in Subsection 3.2. Specifically, we treat these features as binary, indicating their presence or absence at the crime scene.

## 3.1 Logical Reasoning Fundamentals

When people lose lives on the road following traffic accidents, we can say it is a case of road homicide (detailed definition on the Italian ones by S. Balzani, 2016; G. Musco, 2013; P. M. Sabella, 2022) if a violation of one or more norms of the Italian Road Code law occurs when the obstacle (i.e., the victim) was foreseeable and avoidable (if the defendant had diligent behavior). The logical reasoning for a road homicide crime is according to the occurrence of three conditions (R. Giovagnoli, 2021; 2023):

1. Violation of the Italian Road Code law number 285/1992 norms (e.g., art. 145 penalizing those who fail to comply with a stop signal, art. 146 penalizing those who fail to observe a red light, etc.) or if the act was committed with general recklessness, incompetence, or negligence;

2. The obstacle (i.e., the victim) was foreseeable by the defendant if ordinary diligence had been exercised;

3. The obstacle could have been avoided if the defendant had behaved diligently.

Examining the crime scene and identifying all pertinent factors is crucial for evaluating these conditions. For instance, if the incident occurred during daylight and the victim (i.e., the obstacle to the defendant) was correctly using a crosswalk, the defendant should have been able to foresee and avoid the obstacle. Conversely, if the incident occurred at night and the victim abruptly crossed the road in a non-crosswalk area, the victim would likely not have been easily visible, indicating that the obstacle (i.e., the victim) was neither foreseeable nor avoidable. By applying this logical analysis to the crime scene elements, the investigating judge reconstructs the events and determines whether the three essential conditions were met, thus evaluating the case's circumstances [16].

To test and evaluate our methodology, we selected 100 road homicide cases from open public access Italian portals DeJure Giuffre Francis Lefebvre and Legisway, All-in-One Legal Management Software Wolters Kluwer, both containing texts describing the crime scenes. We referred to road homicide cases that occurred between 2016 and 2024. The data sample was randomly selected. The documents describing the crime scenes were passed to a Large Language Model GPT-based (one by one), asking it to read the text and to build a 25-bit vector for each sample, extracting the 25 predetermined important features. After analyzing all the texts, we built our test dataset containing 100 samples, each with 25 binary values. The last phase of the proposed methodology evaluates the accuracy of the DAJE hardware implementation in predicting the legal response.
## 3.2 The Digital Assurance Judicial Enforcer Design

The hardware circuit assesses the potential road homicide crimes, evaluating key factors reported in Table 1:

<table><caption>Table 1. Possible Crime True Table</caption><tr><td>Abrupt defendant driving:</td><td>The defendant made a sudden maneuver.</td></tr><tr><td>Defendant driving without a valid license</td><td>The defendant was driving without a valid license</td></tr><tr><td>Defendant fleeing</td><td>The defendant fled after the incident.</td></tr><tr><td>Defendant invading</td><td>The defendant invaded the opposite lane.</td></tr><tr><td>Defendant red light violation</td><td>The defendant did not stop at the red light</td></tr><tr><td>Defendant speeding</td><td>The defendant was driving at a speed exceeding legal limits</td></tr><tr><td>Defendant stop violation</td><td>The defendant did not stop at the stop sign</td></tr><tr><td>Defendant vehicles not authorized to operate</td><td>The defendant was driving a vehicle that was not insured or not authorized to operate</td></tr><tr><td>Defendant with phone</td><td>The defendant was using a phone while driving</td></tr><tr><td>Drunk or drugged defendant</td><td>The defendant was under the influence of alcohol or drugs</td></tr><tr><td>Defendant other violation(s)</td><td>The defendant violated the road traffic rule (a generic one or considered as another feature)</td></tr><tr><td>Abrupt victim driving</td><td>The victim made a sudden maneuver</td></tr><tr><td>Drunk or drugged victim</td><td>The victim was under the influence of alcohol or drugs.</td></tr><tr><td>No victim reflective jacket</td><td>The victim was not wearing reflective clothing to be noticed while he/she was on the road where it was necessary</td></tr><tr><td>No victim safety belt</td><td>The victim was not wearing a safety belt</td></tr><tr><td>Red light victim crossing</td><td>The victim crossed during the red light</td></tr><tr><td>Victim driving without a valid license</td><td>The victim was driving without a valid license.</td></tr><tr><td>Victim red light violation</td><td>The victim did not stop at the red light</td></tr><tr><td>Victim speeding</td><td>The victim was driving at a speed exceeding legal limits</td></tr><tr><td>Victim stop violation</td><td>The victim did not stop at the stop sign.</td></tr><tr><td>Victim vehicles not authorized to operate</td><td>The victim was driving a vehicle that was not insured or not authorized to operate</td></tr><tr><td>Victim other violation(s):</td><td>The victim violated the road traffic rule (a generic one or considered as another feature)</td></tr><tr><td>Avoidable obstacle by the defendant</td><td>The defendant could have avoided the impact on the victim if they had been diligent</td></tr><tr><td>Foreseeable obstacle by the defendant</td><td>The defendant could have foreseen the victim's approach if they had been diligent</td></tr></table>

The 25 features could be automatically extracted from crime scene descriptions using advanced NLP techniques in a potential practical application in courts. This extraction process would serve as a preliminary step, feeding the DAJE engine with the necessary data for the subsequent evaluation based on the innovative use of Boolean logic. This approach would integrate artificial intelligence technologies with the efficiency of hardware logic, providing a high-performance system to support judicial decision-making. We treat these features as binary, indicating their presence or absence at the crime scene. The factors in evaluating the possible crime are the Foreseeable Obstacle by the defendant (FO), the Avoidable Obstacle by the defendant (AO), the Defendant's Violation (DV), and the Victim's Violation (VV). For example, in case of safety violations or substance abuse by the defendant (from 1st to 11th features), DV will be set to true; on the other hand, if the victim commits violations (from 12th to 23rd features), VV takes 1. Table 2 reports the boolean function's core decision-making (where 1 means True; 0 corresponds to False). It is worth mentioning that it is impossible to have a scenario where the obstacle is avoidable but not foreseeable (the rows with Crime equal to - in Table 2). DAJE also considered how many violations the defendant and the victim have committed (NDV and NVV, respectively). These cases correspond to the rows with Crime set to * in Table 2: if $NDV \ge NVV$, Crime = 0; otherwise, Crime = 1.
<table><caption>Table 2. Possible Crime Truth Table</caption><thead><tr><td>FO</td><td>AO</td><td>DV</td><td>VV</td><td>Crime</td></tr></thead><tbody><tr><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr><tr><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>0</td><td>1</td><td>0</td><td>0</td><td>—</td></tr><tr><td>0</td><td>1</td><td>0</td><td>1</td><td>—</td></tr><tr><td>0</td><td>1</td><td>1</td><td>0</td><td>—</td></tr><tr><td>0</td><td>1</td><td>1</td><td>1</td><td>—</td></tr><tr><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td></tr><tr><td>1</td><td>0</td><td>1</td><td>1</td><td>*</td></tr><tr><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td></tr><tr><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Figure 2. The hardware implementation of DAJE</figcaption></figure>

## 3.3 The Boolean Rule-Based Hardware Implementation

The whole architecture of DAJE is reported in Figure 2. In particular, DV and VV consist in the output of OR gates, taking in input the respective violations, counted via NDV and NVV counters.

## 3.4 Evaluation

To test the accuracy of DAJE, we referred to 100 road homicide cases taken randomly from the publicly accessible Italian portals DeJure Giuffrè, Francis Lefebvre and Legisway, All-in-One Legal Management Software Wolters Kluwer. DAJE achieved an accuracy of 86%. In particular, we got True Positive (TP) rate of 73%, corresponding to the cases where DAJE correctly determines that a crime has been committed;
13% of True Negative (TN) corresponding to the cases where DAJE correctly determines that no crime has been committed; 8% of False Positive (FP) rate, when no crime occurred, but DAJE detects one, and 6% of False Negative (FN) rate, indicating cases where a crime occurred, but our DAJE did not detect it.

## 4. CONCLUSION

We proposed DAJE, the first hardware circuit specifically designed to emulate the legal logic of road homicides according to Italian law. Our proposed hardware solution leverages the inherent advantages of hardware implementations, such as enhanced security and performance, to address these needs. DAJE uses Boolean logic to model the legal reasoning required for road homicide cases in Italy. By encoding the legal conditions and factors involved in such incidents, the DAJE circuit provides a reliable and transparent method for legal decision-making. Our implementation utilizes 6 LUTs and 24 FFs, achieving a maximum operating frequency of 642 MHz and an average power consumption of 89 mW on FPGA emulation. The experimental results demonstrated that DAJE replicates the legal logic applied in real road homicide cases in Italy with an accuracy of 86%. This confirms the viability of hardware-based solutions for legal applications, offering significant potential for improving the security and efficiency of digital legal systems. Applications close to the DAJE paradigm could be useful paradigms that support workers in the justice domain. Indeed, the objective of this paper is not only to introduce a novel concept that bridges hardware and the legal domain but also to inspire future research that explores the intersection between these fields.

In conclusion, DAJE represents a pioneering step towards integrating advanced hardware implementations in the legal sector. We envision this work as the starting point for a new line of research that combines hardware and legal logic, and we hope to serve as pioneers in this emerging field. Furthermore, we encourage the scientific community to delve deeper into this topic, studying and developing other hardware-based solutions that implement legal functions. Future research could expand this approach to other areas of law, refining the hardware design while maintaining the same paradigm on digital systems in legal contexts.

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