IDEAS home Printed from https://ideas.repec.org/a/vrs/ceuecj/v11y2024i58p214-232n1015.html
   My bibliography  Save this article

Predicting the Amount of Compensation for Harm Awarded by Courts Using Machine-Learning Algorithms

Author

Listed:
  • Świtała Maciej

    (University of Warsaw, Faculty of Economic Sciences, Długa 44/50, 00-241 Warsaw, Poland)

Abstract

The present study aims to explain and predict the monetary amount awarded by courts as compensation for harm suffered. A set of machine-learning algorithms was applied to a sample of decisions handed down by the Polish common courts. The methodology involved two steps: identification of words and phrases whose counts or frequencies affect the amounts adjudicated with LASSO regression and expert assessment, then applying OLS, again LASSO, random forests and XGBoost algorithms, as well as a BERT approach to make predictions. Finally, an in-depth analysis was undertaken on the influence of individual words and phrases on the amount awarded. The results demonstrate that the size of awards is most strongly influenced by the type of injury suffered, the specifics of treatment, and the family relationship between the harmed party and the claimant. At the same time, higher values are awarded when compensation for material damage and compensation for harm suffered are claimed together or when the claim is extended after it was filed.

Suggested Citation

  • Świtała Maciej, 2024. "Predicting the Amount of Compensation for Harm Awarded by Courts Using Machine-Learning Algorithms," Central European Economic Journal, Sciendo, vol. 11(58), pages 214-232.
    • Handle: RePEc:vrs:ceuecj:v:11:y:2024:i:58:p:214-232:n:1015
    DOI: 10.2478/ceej-2024-0015
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/ceej-2024-0015
    Download Restriction: no
    File URL: https://libkey.io/10.2478/ceej-2024-0015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Daniel Martin Katz & Michael J Bommarito II & Josh Blackman, 2017. "A general approach for predicting the behavior of the Supreme Court of the United States," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-18, April.
    2. Odey Alshboul & Mohammad A. Alzubaidi & Rabia Emhamed Al Mamlook & Ghassan Almasabha & Ali Saeed Almuflih & Ali Shehadeh, 2022. "Forecasting Liquidated Damages via Machine Learning-Based Modified Regression Models for Highway Construction Projects," Sustainability, MDPI, vol. 14(10), pages 1-25, May.
    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. Davis, Yehuda & Govindaraj, Suresh & Suslava, Kate, 2024. "Does the stock market anticipate events and supreme court decisions in corporate cases?," Global Finance Journal, Elsevier, vol. 60(C).
    2. Anthony Niblett, 2018. "Regulatory Reform in Ontario: Machine Learning and Regulation," C.D. Howe Institute Commentary, C.D. Howe Institute, issue 507, March.
    3. Yıldırım, Engin & Sert, Mehmet Fatih & Kartal, Burcu & Çalış, Şuayyip, 2023. "Non-compliance of the European Court of Human Rights decisions: A machine learning analysis," International Review of Law and Economics, Elsevier, vol. 76(C).
    4. Alain Marciano & Antonio Nicita & Giovanni Battista Ramello, 2020. "Big data and big techs: understanding the value of information in platform capitalism," European Journal of Law and Economics, Springer, vol. 50(3), pages 345-358, December.
    5. Luis Enriquez, 2024. "A Personal data Value at Risk Approach," Papers 2411.03217, arXiv.org, revised Nov 2024.
    6. Bokwon Lee & Kyu-Min Lee & Jae-Suk Yang, 2019. "Network structure reveals patterns of legal complexity in human society: The case of the Constitutional legal network," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-15, January.
    7. Daniyal Alghazzawi & Omaimah Bamasag & Aiiad Albeshri & Iqra Sana & Hayat Ullah & Muhammad Zubair Asghar, 2022. "Efficient Prediction of Court Judgments Using an LSTM+CNN Neural Network Model with an Optimal Feature Set," Mathematics, MDPI, vol. 10(5), pages 1-30, February.
    8. Yang, Guancan & Lu, Guoxuan & Xu, Shuo & Chen, Liang & Wen, Yuxin, 2023. "Which type of dynamic indicators should be preferred to predict patent commercial potential?," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    9. Carmen Vargas Pérez & Juan Luis Peñaloza Figueroa, 2023. "Tracking the New Demand for Justice in the Big Data Ecosystem," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 6, ejme_v6_i.
    10. Frederike Zufall & Rampei Kimura & Linyu Peng, 2021. "Towards a simple mathematical model for the legal concept of balancing of interests," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2021_09, Max Planck Institute for Research on Collective Goods, revised 19 Oct 2021.
    11. Odey Alshboul & Ali Shehadeh & Rabia Emhamed Al Mamlook & Ghassan Almasabha & Ali Saeed Almuflih & Saleh Y. Alghamdi, 2022. "Prediction Liquidated Damages via Ensemble Machine Learning Model: Towards Sustainable Highway Construction Projects," Sustainability, MDPI, vol. 14(15), pages 1-23, July.
    12. Małgorzata Dobrowolska & Mariola Paruzel-Czachura & Marta Stasiła-Sieradzka, 2018. "Perception of Limitations by Individuals Threatened with Social Exclusion upon Entering Employment: Report on a Study of Individuals with Chronic Mental Illnesses," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 4, ejes_v4_i.
    13. Amedeo Santosuosso & Giulia Pinotti, 2020. "Bottleneck or Crossroad? Problems of Legal Sources Annotation and Some Theoretical Thoughts," Stats, MDPI, vol. 3(3), pages 1-20, September.
    14. Ulenaers Jasper, 2020. "The Impact of Artificial Intelligence on the Right to a Fair Trial: Towards a Robot Judge?," Asian Journal of Law and Economics, De Gruyter, vol. 11(2), pages 1, August.
    15. Zhong, Weifeng & Chan, Julian, 2020. "Predicting Authoritarian Crackdowns: A Machine Learning Approach," Working Papers 10464, George Mason University, Mercatus Center.
    16. Bruno Mathis, 2022. "Extracting Proceedings Data from Court Cases with Machine Learning," Stats, MDPI, vol. 5(4), pages 1-16, December.
    17. Bălan Carmen, 2018. "The Impact of Conversational Agents on Humans in Services: Research Questions and Hypotheses," International Conference on Marketing and Business Development Journal, The Bucharest University of Economic Studies, vol. 1(2), pages 33-55, December.
    18. So-Hui Park & Dong-Gu Lee & Jin-Sung Park & Jun-Woo Kim, 2021. "A Survey of Research on Data Analytics-Based Legal Tech," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    19. , Aisdl, 2020. "Becoming Attuned," OSF Preprints j7f8y, Center for Open Science.
    20. Odey Alshboul & Ali Shehadeh & Ghassan Almasabha & Ali Saeed Almuflih, 2022. "Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, May.

    More about this item

    Keywords

    compensation amount; harm; machine learning; Polish courts; prediction;
    All these keywords.

    JEL classification:

    • K15 - Law and Economics - - Basic Areas of Law - - - Civil Law; Common Law

    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:vrs:ceuecj:v:11:y:2024:i:58:p:214-232:n:1015. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.