Challenges and Opportunities for Visual Analytics in Jurisprudence
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914163703414784 |
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| author | Fürst, Daniel El-Assady, Mennatallah Keim, Daniel A. Fischer, Maximilian T. |
| author_facet | Fürst, Daniel El-Assady, Mennatallah Keim, Daniel A. Fischer, Maximilian T. |
| contents | Legal exploration, analysis, and interpretation remain complex and demanding tasks, even for experienced legal scholars, due to the domain-specific language, tacit legal concepts, and intentional ambiguities embedded in legal texts. In related, text-based domains, Visual Analytics (VA) has become an indispensable tool for navigating documents, representing knowledge, and supporting analytical reasoning. However, legal scholarship presents distinct challenges: it requires managing formal legal structure, drawing on tacit domain knowledge, and documenting intricate and accurate reasoning processes - needs that current VA system designs for law fail to address adequately. We identify and describe key challenges and underexplored opportunities in applying VA to law, exploring how these technologies might better serve the legal domain. Interviews with nine legal experts reveal that current legal information retrieval interfaces do not adequately support the navigational complexity of law, often forcing users to rely on internalized legal expertise instead. To address this gap, we identify a three-phase workflow for legal experts, which highlights opportunities for VA to support legal reasoning through knowledge externalization and provenance tracking, leveraging tree-, graph-, and hierarchy-based visualizations. Through this contribution, our work establishes a user-centered VA workflow for the legal domain, recognizing tacit legal knowledge as a critical element of sense-making and insight generation, and situates these contributions within a broader research agenda for VA in law and other text-based disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06543 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Challenges and Opportunities for Visual Analytics in Jurisprudence Fürst, Daniel El-Assady, Mennatallah Keim, Daniel A. Fischer, Maximilian T. Human-Computer Interaction H.5.2 Legal exploration, analysis, and interpretation remain complex and demanding tasks, even for experienced legal scholars, due to the domain-specific language, tacit legal concepts, and intentional ambiguities embedded in legal texts. In related, text-based domains, Visual Analytics (VA) has become an indispensable tool for navigating documents, representing knowledge, and supporting analytical reasoning. However, legal scholarship presents distinct challenges: it requires managing formal legal structure, drawing on tacit domain knowledge, and documenting intricate and accurate reasoning processes - needs that current VA system designs for law fail to address adequately. We identify and describe key challenges and underexplored opportunities in applying VA to law, exploring how these technologies might better serve the legal domain. Interviews with nine legal experts reveal that current legal information retrieval interfaces do not adequately support the navigational complexity of law, often forcing users to rely on internalized legal expertise instead. To address this gap, we identify a three-phase workflow for legal experts, which highlights opportunities for VA to support legal reasoning through knowledge externalization and provenance tracking, leveraging tree-, graph-, and hierarchy-based visualizations. Through this contribution, our work establishes a user-centered VA workflow for the legal domain, recognizing tacit legal knowledge as a critical element of sense-making and insight generation, and situates these contributions within a broader research agenda for VA in law and other text-based disciplines. |
| title | Challenges and Opportunities for Visual Analytics in Jurisprudence |
| topic | Human-Computer Interaction H.5.2 |
| url | https://arxiv.org/abs/2412.06543 |