Knowledge and understanding: Students should demonstrate a good knowledge of the comparative legal methodology and of its applications to emerging technologies, as well as a good understanding of the main analytical and statistical methods to analyze big data and open data. Applying knowledge and understanding: Students should be able to adopt the necessary methodological and practical tools to analyze different kinds of big data, as well as demonstrate a good knowledge of the main rules applicable at the EU and at the national level on data-driven digital technologies, and of the most frequent argumentations applicable to legal issues concerning emerging technologies. Making judgements: Students should have the ability to organise the acquired knowledge and to make judgments on regulatory issues and social implications of emerging technologies on the basis of limited and incomplete information. Similarly, they should develop the ability to select the appropriate techniques for BDA and compare results obtained by competing methods. Communication skills: Students should have the ability to discuss and present the main methodologies and the expected results of the statistical techniques available for BDA, and they should be able to present their own view vis-à-vis the general features and contents of the main rules applicable at the European Union and at the national level to data-driven emerging digital technologies. Learning skills: Students should have the ability to understand the key literature about BDA for sustainability and social inclusion, as well as to to articulate and interpret European Union and European national notions and techniques pertinent to the rules applicable to data-driven digital technologies, including artificial intelligence, and to the different concepts and theories related to this field.

A basic knowledge of descriptive statistics and quantitative methods is recommended. Students should be familiar with basic statistical inference, including confidence intervals and hypothesis testing.

The course consists of two modules: (1) Module “European and comparative law of digital technologies” provides an advanced overview of the main legal framework applying to data-driven emerging digital technologies, with a focus on Artificial intelligence, in the European context, with regard to both national, European Union and international rules. (2) Module “Big data and open data for sustainability and social inclusion” will offer both theoretical and practical insights into Big Data Analysis (BDA)-based methodologies as tools for promoting social sustainability and inclusion. Real-life case studies will be used to illustrate in practice the methodology and discuss the obtained results. Particular attention will be given to discuss the added value of using BDA technology to achieve the Sustainable Development Goals (SDGs) as set in the United Nations 2030 agenda.

For the course materials, please refer to each module’s syllabus.

The course consists of two modules: (1) Module “European and comparative law of digital technologies” provides an advanced overview of the main legal framework applying to data-driven emerging digital technologies, with a focus on Artificial intelligence, in the European context, with regard to both national, European Union and international rules. (2) Module “Big data and open data for sustainability and social inclusion” will offer both theoretical and practical insights into Big Data Analysis (BDA)-based methodologies as tools for promoting social sustainability and inclusion. Real-life case studies will be used to illustrate in practice the methodology and discuss the obtained results. Particular attention will be given to discuss the added value of using BDA technology to achieve the Sustainable Development Goals (SDGs) as set in the United Nations 2030 agenda.

Lectures, open discussions, practical sessions, individual and group activities.

The final grade of the course will be the average of the grades obtained for each module. Overall, students will pass the test if their final grade is equal or above 18/30.

SI