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Explainable artificial intelligence for assault sentence prediction in New Zealand
Harry Rodger1,2, Andrew Lensen1, Marcin Betkier2
1School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand.
Journal of the Royal Society of New Zealand
|October 23, 2024
Summary
This study explores using explainable artificial intelligence (AI) to predict prison sentences for assault cases in New Zealand. The developed AI model accurately predicts sentences, offering insights into influential factors.
Area of Science:
- Computational Social Science
- Legal Technology
- Artificial Intelligence Ethics
Background:
- Judicial systems traditionally exhibit conservatism regarding Artificial Intelligence (AI) adoption.
- Recent machine learning advancements encourage exploring AI for legal tasks like sentence prediction.
- Explainable AI (XAI) offers transparency crucial for judicial applications.
Purpose of the Study:
- To investigate the feasibility of using explainable AI for predicting imprisonment sentences in New Zealand assault cases.
- To develop and validate a proof-of-concept XAI model for sentence prediction.
- To analyze the key factors influencing sentence length predictions.
Main Methods:
- Development of a proof-of-concept explainable AI model.
- Experimental validation of the model's predictive accuracy in New Zealand assault cases.
- Analysis of model outputs to identify influential phrases in sentence length determination.
Main Results:
- The proposed explainable AI model demonstrates practical utility and fitness for purpose.
- Predicted sentences achieved accuracy within a one-year margin.
- Identification of specific phrases significantly influencing sentence length predictions.
Conclusions:
- Explainable AI holds potential for enhancing sentence prediction in the New Zealand judiciary.
- The study provides a foundation for discussing the benefits and risks of AI in legal sentencing.
- Further research is warranted to explore responsible implementation of AI in court systems.
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