Uniwersytet Warszawski, Wydział Nauk Ekonomicznych - Centralny System Uwierzytelniania
Strona główna

Introduction to legal data analysis

Informacje ogólne

Kod przedmiotu: 2400-ZEWW918-OG
Kod Erasmus / ISCED: 14.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0311) Ekonomia Kod ISCED - Międzynarodowa Standardowa Klasyfikacja Kształcenia (International Standard Classification of Education) została opracowana przez UNESCO.
Nazwa przedmiotu: Introduction to legal data analysis
Jednostka: Wydział Nauk Ekonomicznych
Grupy: Przedmioty ogólnouniwersyteckie na Uniwersytecie Warszawskim
Przedmioty ogólnouniwersyteckie Wydziału Nauk Ekonomicznych
Punkty ECTS i inne: (brak) Podstawowe informacje o zasadach przyporządkowania punktów ECTS:
  • roczny wymiar godzinowy nakładu pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się dla danego etapu studiów wynosi 1500-1800 h, co odpowiada 60 ECTS;
  • tygodniowy wymiar godzinowy nakładu pracy studenta wynosi 45 h;
  • 1 punkt ECTS odpowiada 25-30 godzinom pracy studenta potrzebnej do osiągnięcia zakładanych efektów uczenia się;
  • tygodniowy nakład pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się pozwala uzyskać 1,5 ECTS;
  • nakład pracy potrzebny do zaliczenia przedmiotu, któremu przypisano 3 ECTS, stanowi 10% semestralnego obciążenia studenta.

zobacz reguły punktacji
Język prowadzenia: angielski
Rodzaj przedmiotu:


Skrócony opis: (tylko po angielsku)

This course is designed to provide an introduction to the use of empirical data in solving legal problems. It will first discuss how to plan and conduct empirical legal research and how to implement specific solutions based on it, including in business. Then the basics of qualitative and quantitative methods that can be used in such research will be presented. Consideration of the latter will include a brief presentation of the possibilities of using artificial intelligence, in particular machine learning algorithms and natural language processing. In the remaining part of the course, a discussion of selected case studies is planned. Most importantly, the results of empirical studies of factors influencing judges' decisions, prediction of court decisions in specific cases, as well as ways to evaluate the effectiveness of the judicial system, legal offices and the law itself will be presented.

Pełny opis: (tylko po angielsku)

1. Introductory matters.

During the first class, it is planned to present the importance of law as a social science and its inextricable connection with the disciplines of economics, psychology and sociology. This combination implies an undeniable diversity of empirical legal studies, both in terms of the issues addressed and the methods applied. In addition, the attention of the course participants will be drawn to the possible practical usage of empirical legal studies, most prominently in the broadly understood legal business.

2. Planning an empirical legal study.

Planning an empirical legal study requires certain preparatory steps. To put it in scientific language: identifying research questions, conducting literature review, formulation of research hypotheses, research design, sampling, identification of research risks and their minimisation, data collection and ethical considerations shall be covered in this part. Translating the above to business language: it is necessary to accurately identify the business problem, analyse strengths, weaknesses, opportunities and threats of specific solutions, choose appropriate tools, test and implement them, which involves training the staff, continuous monitoring, and refining of the applied solution. All the above shall be discussed in this section.

3. Methods of empirical legal studies.

This part of the course shall contain an accessible and understandable presentation of the methods available in empirical legal research. It is planned to discuss both qualitative and quantitative approaches. Most importantly, the introduction to the latter will include a brief discussion of the idea of modelling social phenomena with classical econometric methods, to be followed by a presentation of artificial intelligence tools, more precisely machine learning algorithms. This will be complemented by a discussion of methods of natural language processing.

4. Analysis of legal data on the application of the law.

This section will present examples of empirical research on the application of the law. Firstly, state-of-the-art scientific publications on factors influencing judges' decisions will be discussed. Next, what appears to be of the highest practical importance, models aimed at predicting court decisions in specific factual situations will be reviewed. Finally, studies on the evolution of jurisprudence will be discussed.

5. Analysis of legal data on the effectiveness of the judiciary and legal offices.

This section will introduce the concepts of workload, productivity, efficiency and effectiveness of the courts. Most importantly, it is planned to present and discuss a wide range of measures on these. Next, the most important empirical studies on the assessment of the judicial system will be presented. Finally, it shall be considered how to adapt the discussed methodology to analyse the effectiveness of legal offices.

6. Analysis of legal data on the effectiveness of the law itself.

Within this part of the course, empirical research on the effectiveness of procedures and punishments are planned to be discussed. Also, it will be considered whether the law changes the behaviour of individuals, and whether it solves social problems. Furthermore, this section shall involve presentation of current research on the impact of legal changes on society, economy and environment. Technically, the discussion of the aforementioned issues, given their specificity, will primarily include an introduction to the well-established idea of economic analysis of law.

7. Analysis of legal data on justice, equality and perception of the law.

This class will focus on reporting on the current state of empirical research on inequalities in the legal system. In particular, the problem of inequality in access to justice will be considered. Also, addressing the psychological and sociological elements of empirical legal studies, the current status of research on the public perception of the law, the judiciary and individual legal regulations will be reviewed.

8. Students’ presentations.

Literatura: (tylko po angielsku)


Barry, B. M. (2020). How judges judge: Empirical insights into judicial decision-making. Informa Law from Routledge.

Clarke, V., & Braun, V. (2013). Successful qualitative research: A practical guide for beginners.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference and prediction. Springer Series in Statistics.

Kaplow, L., & Shavell, S. (2002). Economic analysis of law. In Handbook of public economics (Vol. 3, pp. 1661-1784). Elsevier.

Posner, R. A. (2014). Economic analysis of law. Aspen Publishing.

Varian, H. R. (2003). Intermediate microeconomics: a modern approach. Elsevier.


Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., & Lampos, V. (2016). Predicting judicial decisions of the European Court of Human Rights: A natural language processing perspective. PeerJ computer science, 2, e93.

Ash, E., Chen, D. L., & Galletta, S. (2022). Measuring judicial sentiment: Methods and application to us circuit courts. Economica, 89(354), 362-376.

Banasik, P., Metelska-Szaniawska, K., Godlewska, M., & Morawska, S. (2021). Determinants of judges’ career choices and productivity: a Polish case study, European Journal of Law and Economics, 53, 81-107.

Bełdowski, J., Dąbroś, Ł. & Wojciechowski, W. (2020). Judges and court performance: a case study of district commercial courts in Poland. European Journal of Law and Economics, 50, 171–201.

Carlson, K., Livermore, M. A., & Rockmore, D. N. (2020). The problem of data bias in the pool of published US appellate court opinions. Journal of Empirical Legal Studies, 17(2), 224-261.

Carter, D. J., Brown, J., & Rahmani, A. (2016). Reading the High Court at a distance: topic modelling the legal subject matter and judicial activity of the High Court of Australia, 1903-2015. The University of New South Wales Law Journal, 39(4), 1300-1354.

Danziger, S., Levav, J., & Avnaim-Pesso, L. (2011). Extraneous factors in judicial decisions. Proceedings of the National Academy of Sciences, 108(17), 6889-6892.

Grajzl, P., & Murrell, P. (2021). A machine-learning history of English caselaw and legal ideas prior to the Industrial Revolution I: generating and interpreting the estimates. Journal of Institutional Economics, 17(1), 1-19.

Katz, D. M., Bommarito, M. J., & Blackman, J. (2017). A general approach for predicting the behavior of the Supreme Court of the United States. PloS one, 12(4), e0174698.

Liu, J. Z., & Li, X. (2019). Legal techniques for rationalizing biased judicial decisions: Evidence from experiments with real judges. Journal of Empirical Legal Studies, 16(3), 630-670.

Marciano, A., Melcarne, A., & Ramello, G. B. (2019). The economic importance of judicial institutions, their performance and the proper way to measure them. Journal of Institutional Economics, 15(1), 81-98.

Medvedeva, M., Vols, M., & Wieling, M. (2020). Using machine learning to predict decisions of the European Court of Human Rights. Artificial Intelligence and Law, 28, 237-266.

Sulea, O. M., Zampieri, M., Malmasi, S., Vela, M., Dinu, L. P., & Van Genabith, J. (2017). Exploring the use of text classification in the legal domain. arXiv preprint, arXiv:1710.09306.

Voigt, S. (2016). Determinants of judiciary efficiency: A survey. European Journal of Law and Economics, 42(2), 183-208.

Efekty uczenia się: (tylko po angielsku)

Upon completion of the course, students will be aware of the connections between the disciplines of the social sciences and the diversity of empirical legal studies. Course participants will also gain a basic knowledge of qualitative and quantitative tools that can be used for empirical legal research. In addition, students will be able to orient themselves in the literature on the subject, specifically they will be familiar with current research trends and state-of-the-art approaches to the most prominent research problems. Finally, students will be able to design their own empirical research on legal topics, formulate research hypotheses, select methods appropriate to the purpose of the study. They will also be able to transfer the above from the ground of science to business.

Metody i kryteria oceniania: (tylko po angielsku)

The final grade will be based on a written project of an empirical study of the law (weighting of 80%) and its presentation (weighting of 20%). Still, no presentation means a negative grade from the course.

Przedmiot nie jest oferowany w żadnym z aktualnych cykli dydaktycznych.
Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Warszawski, Wydział Nauk Ekonomicznych.
ul. Długa 44/50
00-241 Warszawa
tel: +48 22 55 49 126 https://www.wne.uw.edu.pl/
kontakt deklaracja dostępności USOSweb (2024-03-22)