IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-04316581.html
   My bibliography  Save this paper

Critical AI Challenges in Legal Practice : An application to French Administrative Decisions

Author

Listed:
  • Khaoula Naili

    (CRESE - Centre de REcherches sur les Stratégies Economiques (UR 3190) - UFC - Université de Franche-Comté - UBFC - Université Bourgogne Franche-Comté [COMUE])

Abstract

We use AI methods to evaluate the accuracy of several standard machine learning models for predicting judicial decision outcomes. We highlight the key steps and challenges in predicting judicial outcomes by applying these models to a database of administrative court decisions.These findings significantly contribute to our understanding of the potential advantages of AI in the context of predictive justice. We utilize AI methods to analyze administrative court decisions sourced from the database provided by the French Council of State. This analysis has been made possible due to the Council of State's decision to make its decisions publicly accessible since March 2022. Our innovative approach pioneers the use of prediction models on the open data from the French Council of State, addressing the complexities associated with data analysis. Our primary objective is to assess the accuracy of these models in predicting outcomes in French administrative tribunals and identify the most effective model for forecasting administrative tribunal court decisions. The selected models are trained and evaluated on multi-class datasets, where decisions are traditionally categorized into various classes.

Suggested Citation

  • Khaoula Naili, 2023. "Critical AI Challenges in Legal Practice : An application to French Administrative Decisions," Working Papers hal-04316581, HAL.
    • Handle: RePEc:hal:wpaper:hal-04316581
    Note: View the original document on HAL open archive server: https://univ-fcomte.hal.science/hal-04316581
    as

    Download full text from publisher

    File URL: https://univ-fcomte.hal.science/hal-04316581/document
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Aaron Shapiro, 2017. "Reform predictive policing," Nature, Nature, vol. 541(7638), pages 458-460, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    2. Ishmael Mugari & Emeka E. Obioha, 2021. "Predictive Policing and Crime Control in The United States of America and Europe: Trends in a Decade of Research and the Future of Predictive Policing," Social Sciences, MDPI, vol. 10(6), pages 1-14, June.
    3. Sukanya Samanta & Goutam Sen & Soumya Kanti Ghosh, 2022. "A literature review on police patrolling problems," Annals of Operations Research, Springer, vol. 316(2), pages 1063-1106, September.
    4. Gian Maria Campedelli, 2021. "Where are we? Using Scopus to map the literature at the intersection between artificial intelligence and research on crime," Journal of Computational Social Science, Springer, vol. 4(2), pages 503-530, November.
    5. Brandt, Tobias & Wagner, Sebastian & Neumann, Dirk, 2021. "Prescriptive analytics in public-sector decision-making: A framework and insights from charging infrastructure planning," European Journal of Operational Research, Elsevier, vol. 291(1), pages 379-393.

    More about this item

    Keywords

    Artificial intelligence AI; Machine learning; Natural language processing; Predictive justice;
    All these keywords.

    JEL classification:

    • K4 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-04316581. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.