Rethinking the field of automatic prediction of court decisions

Medvedeva, Masha; Wieling, Martijn; Vols, Michel

doi:10.1007/s10506-021-09306-3

Cited by 41 publications

(28 citation statements)

References 41 publications

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“…These communications are available well before the Court's decision. As shown in Medvedeva et al (2022), however, the accuracy of forecasting outcomes when using this information is considerably lower than when classifying the cases according to outcome using information available after the trial.…”

Section: Discussionmentioning

confidence: 99%

Thirty years of Artificial Intelligence and Law: the second decade

et al. 2022

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The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal's second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments.

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Section: Discussionmentioning

confidence: 99%

Thirty years of Artificial Intelligence and Law: the second decade

et al. 2022

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“…The court publishes decision texts and 'case detail' tables on its 'HUDOC' database. 4 ECHR cases have been studied in several prior works [7,19] and included in the benchmark LexGLUE [79]. While LexGLUE provides a large number of processed ECHR texts and outcomes, that dataset is not linked to case identifiers, making topic interpretation challenging.…”

Section: (Ii) European Convention On Human Rights Violationsmentioning

confidence: 99%

“…Given their centrality in legal analysis, court decisions in particular have attracted significant scholarly attention. Many studies have attempted to identify, categorize or forecast case outcomes using decision texts, often relying on opaque algorithms such as support vector machines and neural networks [7,[17][18][19][20]. Other researchers have prioritized more explainable methods over end-toend prediction.…”

Section: Introductionmentioning

confidence: 99%

Discovering significant topics from legal decisions with selective inference

Soh Tsin Howe

2024

Phil. Trans. R. Soc. A.

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We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distributions which can be manually interpreted to gain insights about significant topics, and case-topic weights which can be used to identify representative cases for each topic. We demonstrate the method on a new dataset of domain name disputes and a canonical dataset of European Court of Human Rights violation cases. Topic models based on latent semantic analysis as well as language model embeddings are evaluated. We show that topics derived by the pipeline are consistent with legal doctrines in both areas and can be useful in other related legal analysis tasks. This article is part of the theme issue ‘A complexity science approach to law and governance’.

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“…Imagine, for example, that judges could write their judgments using GANs (generative adversarial networks) trained on all the available judgments. Or imagine that you could ‘predict’ the outcome of your dispute (Medvedeva et al, 2023). A statistical analysis of judgments could also help us better to understand the key trends in judicial decision-making, identify court biases, or spot outlier practices.…”

Section: A Little Bit Of Backgroundmentioning

confidence: 99%

Judgments as bulk data

Janeček

2023

Big Data & Society

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Should court judgments be publicly available for text and data mining purposes? This article shows that the arguments for and against access to judgments conflate different understandings of what judgments are. On one view, judgments are seen as a ‘jurisprudential’ category, whereas the other view regards them as something ‘factual’. Once it is understood that these views and the claims based on them do not fight over the same territory, it should be easier to make judgments more widely available, including for the purposes of computational analysis of judgments as bulk data. The purpose of this article is to help to clear the ground for the debate around access to judgments as bulk data and highlight some relevant considerations for the preferred licencing regime concerning judgments.

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Rethinking the field of automatic prediction of court decisions

Cited by 41 publications

References 41 publications

Thirty years of Artificial Intelligence and Law: the second decade

Thirty years of Artificial Intelligence and Law: the second decade

Discovering significant topics from legal decisions with selective inference

Judgments as bulk data

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