Data, algorithms, artificial intelligence: regulatory challenges 1600-SZD-SPEC-DAA-PR
; In recent years, increased availability of data, the development of new analytic tools, and the use of machines capable of future predictions have made learning from data and algorithm-based decision-making a reality. The advancement of machine-learning technologies brings significant benefits to society and the economy, while improving the lives of individuals, by leading to a more competitive industry, a more efficient public administration, a more resilient democratic process, better healthcare, safer transport, etcetera. At the same time, this new environment raises several risks and regulatory challenges, concerning, among others, the control and availability of data, the non-transparency of algorithms, liability issues raised by machine-learning technologies, the role of creativity and inventiveness in machine-generated contents, the integrity of personal data, etcetera. Against this background, the course provides a critical view on the legal implications of big data, algorithms and artificial intelligence and critically discusses how the current EU legal framework is addressing the challenges they raise in order to properly balance the need to mitigate the risks while, at the same time, enhancing the innovation they generate.
Course coordinators
Learning outcomes
Knowledge:
WK_1 Knows and understands fundamental dilemmas of modern civilization.
WK_2 Knows and understands economic, legal, ethical and other significant determinants of academic activity
WK_3 Knows and understands basic principles of knowledge transfer to the economic and social spheres as well as commercialisation of research results and the know-how related to them
Skills:
UK._5 Can use a foreign language at the B2 level, according to Common European Framework of Reference for Languages, enabling participation in the international scientific and professional environment
After completing the course, a student will:
- Understand the policy relevance of big data and emerging digital technologies;
- Frame the legal challenges raised by them;
- Discuss the solutions currently adopted at European level;
- Identify the relationship between the solutions under study and European policies;
- Apply the current legal framework to real-life scenarios.
Assessment criteria
description of requirements related to participation in classes, including the
permitted number of explained absences; Active participation is required, and one explained absence is allowed.
principles for passing the classes and the subject (including resit session); Active participation in classes.
methods for the verification of learning outcomes; Active participation in classes.
evaluation criteria Understanding of the topics covered in the course and the ability to address them critically will be assessed, through consideration of participation in class and the remarks provided in relation to the reading materials.
Practical placement
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Bibliography
Due to the rapidly evolving subject matter of the course, assigned readings are shared on a weekly basis. A list of supporting materials is available below: 1. Bart van der Sloot and Sascha van Schendel, Ten Questions for Future Regulation of Big Data: A Comparative and Empirical Legal Study, 7/2016, JIPITEC, 110;
2. L. Zoboli, Fueling the European Digital Economy: A Regulatory Assessment of B2B Data Sharing, in European Business Law Review, 4/2020, 663.
3. Rolf H. Weber, Liability in the Internet of Things, EuCML, 5/2017, 207;
4. Alžběta Krausová, Intersections between Law and Artificial Intelligence, International Journal of Computer (IJC) Vol. 27, 2017, 55;
5. Eleonora Rosati, The Monkey Selfie case and the concept of authorship: an EU perspective, Journal of Intellectual Property Law & Practice, 2017, Vol. 12, No. 12;
6. Ugo Pagallo, The Legal Challenges of Big Data: Putting Secondary Rules First in the Field of EU Data Protection, EDPL 1/2017.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: