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        <title>Legal Judgment Prediction: If You Are Going to Do It, Do It Right</title>
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            <title>Proceedings of the Natural Legal Language Processing Workshop 2023</title>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Preo\textcommabelowtiuc-Pietro</namePart>
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        <name type="personal">
            <namePart type="given">Catalina</namePart>
            <namePart type="family">Goanta</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ilias</namePart>
            <namePart type="family">Chalkidis</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Leslie</namePart>
            <namePart type="family">Barrett</namePart>
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        <name type="personal">
            <namePart type="given">Gerasimos</namePart>
            <namePart type="given">(Jerry)</namePart>
            <namePart type="family">Spanakis</namePart>
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        <name type="personal">
            <namePart type="given">Nikolaos</namePart>
            <namePart type="family">Aletras</namePart>
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    <abstract>The field of Legal Judgment Prediction (LJP) has witnessed significant growth in the past decade, with over 100 papers published in the past three years alone. Our comprehensive survey of over 150 papers reveals a stark reality: only ~7% of published papers are doing what they set out to do - predict court decisions. We delve into the reasons behind the flawed and unreliable nature of the remaining experiments, emphasising their limited utility in the legal domain. We examine the distinctions between predicting court decisions and the practices of legal professionals in their daily work. We explore how a lack of attention to the identity and needs of end-users has fostered the misconception that LJP is a near-solved challenge suitable for practical application, and contributed to the surge in academic research in the field. To address these issues, we examine three different dimensions of ‘doing LJP right’: using data appropriate for the task; tackling explainability; and adopting an application-centric approach to model reporting and evaluation. We formulate a practical checklist of recommendations, delineating the characteristics that are required if a judgment prediction system is to be a valuable addition to the legal field.</abstract>
    <identifier type="citekey">medvedeva-mcbride-2023-legal</identifier>
    <identifier type="doi">10.18653/v1/2023.nllp-1.9</identifier>
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        <date>2023-12</date>
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            <start>73</start>
            <end>84</end>
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%0 Conference Proceedings
%T Legal Judgment Prediction: If You Are Going to Do It, Do It Right
%A Medvedeva, Masha
%A Mcbride, Pauline
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Goanta, Catalina
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Spanakis, Gerasimos (Jerry)
%Y Aletras, Nikolaos
%S Proceedings of the Natural Legal Language Processing Workshop 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F medvedeva-mcbride-2023-legal
%X The field of Legal Judgment Prediction (LJP) has witnessed significant growth in the past decade, with over 100 papers published in the past three years alone. Our comprehensive survey of over 150 papers reveals a stark reality: only ~7% of published papers are doing what they set out to do - predict court decisions. We delve into the reasons behind the flawed and unreliable nature of the remaining experiments, emphasising their limited utility in the legal domain. We examine the distinctions between predicting court decisions and the practices of legal professionals in their daily work. We explore how a lack of attention to the identity and needs of end-users has fostered the misconception that LJP is a near-solved challenge suitable for practical application, and contributed to the surge in academic research in the field. To address these issues, we examine three different dimensions of ‘doing LJP right’: using data appropriate for the task; tackling explainability; and adopting an application-centric approach to model reporting and evaluation. We formulate a practical checklist of recommendations, delineating the characteristics that are required if a judgment prediction system is to be a valuable addition to the legal field.
%R 10.18653/v1/2023.nllp-1.9
%U https://aclanthology.org/2023.nllp-1.9
%U https://doi.org/10.18653/v1/2023.nllp-1.9
%P 73-84

Markdown (Informal)

[Legal Judgment Prediction: If You Are Going to Do It, Do It Right](https://aclanthology.org/2023.nllp-1.9) (Medvedeva & Mcbride, NLLP-WS 2023)

• Legal Judgment Prediction: If You Are Going to Do It, Do It Right (Medvedeva & Mcbride, NLLP-WS 2023)

ACL

• Masha Medvedeva and Pauline Mcbride. 2023. Legal Judgment Prediction: If You Are Going to Do It, Do It Right. In Proceedings of the Natural Legal Language Processing Workshop 2023, pages 73–84, Singapore. Association for Computational Linguistics.