@inproceedings{ad43db4fa536405184016a05d82c845d,
title = "Taking the Law More Seriously by Investigating Design Choices in Machine Learning Prediction Research",
abstract = "Approaches to court case prediction using machine learning differ widely with varying levels of success and legal reasonableness. In part this is due to some aspects of law, such as justification, being inherently difficult for machine learning approaches. Another aspect is the effect of design choices and the extent to which these are legally reasonable, which has not yet been extensively studied. We create four machine learning models tasked with predicting cases from the European Court of Human Rights and we perform experiments in order to measure the role of the following four design choices and effects: the choice of performance metric; the effect of including different parts of the legal case; the effect of a more or less specialized legal focus; and the temporal effects of the available past legal decisions. Through this research, we aim to study design decisions and their limitations and how they affect the performance of machine learning models.",
keywords = "Court case prediction, design choices, machine learning",
author = "Cor Steging and Silja Renooij and Bart Verheij",
note = "Publisher Copyright: {\textcopyright} 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).; 6th Workshop on Automated Semantic Analysis of Information in Legal Text, ASAIL 2023 ; Conference date: 23-09-2023",
year = "2023",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR Workshop Proceedings",
pages = "49--59",
booktitle = "6th Workshop on Automated Semantic Analysis of Information in Legal Text, ASAIL 2023",
}