The judiciary has historically been conservative in its use of Artificial Intelligence, but recent advances in machine learning have prompted scholars to reconsider such use in tasks like sentence prediction. This paper investigates by experimentation the potential use of explainable artificial intelligence for predicting imprisonment sentences in assault cases in New Zealand's courts. We propose a proof-of-concept explainable model and verify in practice that it is fit for purpose, with predicted sentences accurate to within one year. We further analyse the model to understand the most influential phrases in sentence length prediction. We conclude the paper with an evaluative discussion of the future benefits and risks of different ways of using such an AI model in New Zealand's courts.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459755PMC
http://dx.doi.org/10.1080/03036758.2022.2114506DOI Listing

Publication Analysis

Top Keywords

artificial intelligence
12
explainable artificial
8
sentence prediction
8
zealand's courts
8
intelligence assault
4
assault sentence
4
prediction zealand
4
zealand judiciary
4
judiciary historically
4
historically conservative
4

Similar Publications

MultiChem: predicting chemical properties using multi-view graph attention network.

BioData Min

January 2025

Department of Computer Science, Hanyang University, Seoul, Republic of Korea.

Background: Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures.

View Article and Find Full Text PDF

Background: Considering the disruptive potential of AI technology, its current and future impact in healthcare, as well as healthcare professionals' lack of training in how to use it, the paper summarizes how to approach the challenges of AI from an ethical and legal perspective. It concludes with suggestions for improvements to help healthcare professionals better navigate the AI wave.

Methods: We analyzed the literature that specifically discusses ethics and law related to the development and implementation of AI in healthcare as well as relevant normative documents that pertain to both ethical and legal issues.

View Article and Find Full Text PDF

Background: Complete Cytoreduction (CC) in ovarian cancer (OC) has been associated with better outcomes. Outcomes after CC have a multifactorial and interrelated cause that may not be predictable by conventional statistical methods. Artificial intelligence (AI) may be more accurate in predicting outcomes.

View Article and Find Full Text PDF

Resolving tissue complexity by multimodal spatial omics modeling with MISO.

Nat Methods

January 2025

Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets.

View Article and Find Full Text PDF

Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribution of immune dysregulation to PE is not fully understood, highlighting the need to explore molecular markers and their relationship with immune infiltration to potentially inform therapeutic strategies. We used bioinformatics tools to analyze gene expression data from the Gene Expression Omnibus (GEO) database using the GEOquery package in R.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!