96482 - DATA SCIENCE FOR LAWYERS

Course contents

The course wants to cope with the following goals:

1. to provide the knowledge of the technical and legal definitions related to the data analytics, big data, open data, open government data and connected policies;

2. to analyse the normative framework related to the Data Governance (GAIA-X, EU regulation, GDPR, FOIA, PSI II, Licenses, Copyright Directive);
3. to familiarize with some technical instruments for data analytics as R, KNIME, Phyton;
4. to design a data science project with legal data with an interdisciplinary methodology;
5. to analyse data science results under the quantitative and qualitative points of view;
6. to evaluate the explicability (XAI and YAI);
7. to make an ethics evaluation in order to discover biases or other issues that could affect the human rights.

At the end of the course, the student has the knowledge, competencies and skills to read and understand a quantitative and qualitative analysis of data from activities related to the world of law (eg, judgments, justice statistics, legislation, administrative acts) in order to improve their legal profession. He is able to understand the data sources, to evaluate their completeness and consistency, as well as the quality in order to create an autonomous and argued opinion. He/she can analyze datasets in the light of data regulation (protection of personal data, licenses, competition, public law) and identify the legal issues to be addressed (anonymization, pseudo-anonymization, encryption).

In the case of the use of artificial intelligence applied to the legal context that makes use of big data, the student can understand how the datasets contribute to the final outcome in order to provide a reasonable explanation of the conclusions. The student is encouraged to discover any cognitive biases introduced in the data that could cause discrimination or in general regulatory violations and ethical criticalities.

Readings/Bibliography

Ashley, Kevin D. Artificial Intelligence and Legal Analytics. Cambridge University Press, 2017.

Livermore, Michael, and Daniel Rockmore, eds. Law as Data: Computation, Text, and the Future of Legal Analysis. SFI Press, 2019.

https://www.datascienceforlawyers.org/

http://www.legalanalyticscourse.com/

https://landers.com.au/legal-insights-news/the-future-of-analytics-in-legal

https://www.knime.com/learning

https://github.com/Liquid-Legal-Institute/Legal-Text-Analytics

https://github.com/echr-od

https://data.europa.eu/euodp/data/dataset/covid-19-documents-on-eur-lex

Teaching methods

We provide slides, readings, exercises, technical manuals using https://virtuale.unibo.it/ and the TEAMS platform.

Considering the interdisciplinary approach and technical content, including the practice sessions, attendance is key.

This course requires an aptitude for pro-active participation with co-working skills.

Assessment methods

The exam requires you to design, implement, document (10 pages) a data-driven project using the competences acquired including the management of some software.

The project includes:

1. legal analysis;

2. ethics analysis;

3. technical analysis;

4. visualization of the results;

5. argumentation of the results.

The project could be carried out in groups of up to three people. The project must be presented orally with the possibility of discussing the theoretical part as well.

Teaching tools

We use the following support:

http://viruale.unibo.it for archiving material, slides, readings;

https://www.knime.com/ for developping scenarios;

https://www.r-project.org/ for statistical tools.

Office hours

See the website of Monica Palmirani

See the website of Salvatore Sapienza