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    Stefano DEPLANO

    Insegnamento di LAW AND ALGORITHMS


    SSD: IUS/01

    CFU: 6,00


    Periodo di Erogazione: Secondo Semestre


    Lingua di insegnamento



    Teaching language



    Algorithms have been infiltrating and governing several aspects of our lives. Governance by algorithms raises many legal challenges: the aim of the course is to understand the legal impact of algorithms on lawyers, judges, public administrations and States.

    Textbook and course materials

    M. Ebers and S. Navas (eds.), Algorithms and Law, Cambridge University Press, 2020.
    D.R. Desai and J.A. Kroll, Trust but Verify: A Guide to Algorithm and Law, 2017, Harvard Journal Law and Technology, 2 ff.
    C.L. Reyes, A Unified Theory of Code-Connected Contracts, 2021, in The Journal of Corporation Law, 982 ff.

    Further articles on specifical research issues will be provided to the students (see ‘Evaluation method’ section).

    Course objectives

    Knowledge and understanding skills. The student must have a good knowledge of the topics indicated in the program; must also have the ability to understand the subject, with regard both to the institutes analyzed both to the principles and rules that govern the current law system. Most important: student will also have to show how to develop own and original ideas.

    Knowledge and understanding skills applied. The student must demonstrate the ability to interpret and apply their knowledge, skills and understanding skills in seeking solutions to problems related to civil law. The student will be able to apply the rules law to concrete and specific contexts by identifying, interpreting and applying the norms which, from time to time, contribute to characterizing the concrete case. In this context, the student must have the ability to draft legal documents in court and out-of-court contexts.

    Judgment autonomy. The student must be able to interpret the rules of current legal system.

    Communicative Skills. Ability to communicate his / her knowledge in a clear and unambiguous way, to express his / her own considerations and conclusions also in the case of working-class debates that may arise during frontal lessons. The student has to be able to expose the acquired knowledge with arguing consistency.

    Ability to learn. The student has to develop the ability to understand the complexity of the legal phenomenon as well as the learning skills that will enable him to continue studying algorthmic law in an autonomous and conscious manner.


    Civil Law, International Law, Public Law

    Teaching methods

    Lectures and ‘flipped’ classes. The course’s 36-hours are developed interactively: students will be expected to do all the reading assignments and come to class prepared to discuss them.

    Evaluation methods

    Students will be assessed on the basis of (2) written and (1) oral assessments.
    The overall mark will result from the weighted average obtained at the various assessments, as follows:

    1) Final written exam: 50% of final mark;
    2) Oral assessment: 25% of final mark;
    3) Final essay: 25% of final mark.

    The final written exam will consist in 4 open questions drawing on the course programme. The oral exam will focus on students’ reading of the course materials. The final essay – around 10,000 words long – will be assigned at the beginning of the course. It will consist in the discussion of one topic to be chosen from a list of research themes set by the lecturer at the beginning of the course.

    Course Syllabus

    Lecture 1 – Robotics and Artificial Intelligence
    Lecture 2 – Regulating AI and Robotics: Ethical and Legal Challenges
    Lecture 3 – Regulating Algorithms – How to De-Mystify the Alchemy of Code?
    Lecture 4 – Automated Decision-Making under Article 22 GDPR
    Lecture 5 – Robot Machines and Civil Liability
    Lecture 6 – Extra-contractual Liability for Wrongs Committed by Autonomous Systems
    Lecture 7 – Control of Algorithms in Financial Markets
    Lecture 8 – Creativity of Algorithms and Copyright
    Lecture 9 – Wake Neutrality' of Artificial Intelligence Devices
    Lecture 10 – The (envisaged) Legal Framework of Commercialisation of Digital Data within the EU
    Lecture 11 – Theory of smart contracts
    Lecture 12 – Discussion of the essays (see ‘Evaluation method’ section)

    The relationship between lectures and credits is as follows:

    Lectures 1 and 2: 1 credit
    Lectures 3 and 4: 1 credit
    Lectures 5 and 6: 1 credit
    Lectures 7 and 8: 1 credit
    Lectures 9 and 10: 1 credit
    Lectures 11 and 12: 1 credit


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