Opening the Black Box: How Boolean AI can Support Legal Analysis

In crime scene scenarios, there are various factors to consider when determining a suspect's guilt. However, the process of extracting and assessing these factors can be time-consuming, often taking years and incurring significant legal expenses. Judges are now exploring the potential of artificial intelligence techniques and machine learning computations within the justice system. Specifically, in the realm of criminal justice, these methodologies have the potential to aid in investigations and decision-making processes. Utilizing machine learning approaches can thus expedite the bureaucratic process, potentially making it more efficient. We introduce an idea of an approach that could provide fast and explainable support in the evaluation of guilt. Our approach relies on computations based on the presence or absence of 44 features describing the crime scene. Then, by a boolean function, we determined the final verdict of the legal case (only a subset of the extracted features are relevant to evaluate the guilt prediction). To demonstrate the practicality of our proposal, we conducted experiments based on 79 road homicide cases in Italy. As a consequence, the boolean evaluation was done according to Italian law principles. With our system, we reached a 83.2% accuracy rate in extracting features from the legal ruling texts and a 69.6% accuracy in guilt prediction.