In an era in which Internet of Things, Artificial Intelligence, Big Data and social networks are revolutionizing economics, politics, culture and personal relationships, the course aims to introduce students to the ethical and legal impacts of information technology and data-driven processes. This course is mainly designed to give students an increased awareness of the role of data scientists and data experts in society, and a better understanding of the main challenges that they face in developing innovative data-driven products and services. Adopting an interdisciplinary approach, the course focuses both on ethical and legal issues, seen through the lens of technology. From this perspective, the first part of the course will provide a general overview of the interplay between law, ethics and technology, discussing the most relevant issues in the ongoing debate on law & ethics with regard to data-intensive systems. In this context, data protection regulations and their application in the AI environment will be addressed, including risk assessment methodologies. The course then will go beyond the traditional sphere of data protection and deal with the impact of data use on fundamental rights and ethical values, considering different proposals and guidelines on data ethics. The second part of the course is focused on the computer science perspective, with the following approach. Students will first analyse and interactively discuss in class the ACM Code of Ethics and Professional and selected case studies: the goal is to acquire a greater awareness of the duties of a professional to understand the consequences for people and society of apparently only technical choices. Then, students will learn methodologies and data analytics tools to design and conduct an impact assessment for data-driven automated decision systems.

The students will acquire a greater awareness of the duties of a professional in the data science context and will understand the impacts on people and society of the current data-driven algorithms and technologies. They will have a greater understanding of the ethical and legal values that should underpin the development of their products and services. Additionally, they will acquire the proper skills to quantitatively estimate discriminate impacts and treatments on people from data-driven tools, and to mitigate them.

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• Data-driven technology and society • Law & technology • Data protection regulation (GDPR) • AI/Big Data regulation • Risk assessment • Ethical and data policies and guidelines • ACM Code of Ethics and Professional • Case studies on data ethics and policy issues • Bias in software systems and data • Statistical formalizations of algorithmic fairness

The course is divided into lectures, students' contributions and group activities will be encouraged

Given the multidisciplinary nature of this course and the evolving regulatory and technology scenario in the field of data protection and data ethics, a detailed bibliography will be provided at the beginning of the course, including lectures, scientific articles and other materials (e.g. links to regulations, reports, talks and conferences). Here we suggest some introductory readings: Council of Europe. 2019. Guidelines on AI and Data Protection https://www.coe.int/en/web/artificial-intelligence/-/new-guidelines-on-artificial-intelligence-and-data-protection High-Level Expert Group on AI presented Ethics. 2019. Guidelines for Trustworthy Artificial Intelligence https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai Mantelero, A. 2019. Report on Artificial Intelligence Artificial Intelligence and Data Protection: Challenges and Possible Remedies https://www.coe.int/en/web/artificial-intelligence/-/new-guidelines-on-artificial-intelligence-and-data-protection Hildebrandt, M. 2019. Closure: on ethics, code and law. In Hildebrandt, M. (ed). Law for Computer Scientists https://lawforcomputerscientists.pubpub.org/pub/nx5zv2ux ACM. 2018. Code of Ethics and Professional Conduct https://www.acm.org/code-of-ethics Mantelero, A. 2017. Regulating Big Data. The guidelines of the Council of Europe in the Context of the European Data Protection Framework. Computer Law and Security Review, 33 (5): 584-602 https://www.academia.edu/34660255/Regulating_big_data._The_guidelines_of_the_Council_of_Europe_in_the_context_of_the_European_data_protection_framework Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. Limitations and Opportunities (2019). Available online on https://fairmlbook. org/ O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.

Modalità di esame: Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo;

The final exam aims to evaluate the students' understanding of the different topics discussed during the course and how much students apply the acquired notions to various cases. The exam is written and is 90 minutes in duration: it is divided into two sections, one focused on legal issues and one on computer science issues. The students should address two questions (which may be case studies) for each of these sections. Each question worth 7,5 points maximum. It is not allowed to communicate with others during the exam, use a phone, and keep and consult notebooks, books, slides, and forms.

Modalità di esame: Prova scritta (in aula); Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo;

The final exam aims to evaluate the students' understanding of the different topics discussed during the course and how much students apply the acquired notions to various cases. The exam is written and is 90 minutes in duration: it is divided into two sections, one focused on legal issues and one on computer science issues. The students should address two questions (which may be case studies) for each of these sections. Each question worth 7,5 points maximum. It is not allowed to communicate with others during the exam, use a phone, and keep and consult notebooks, books, slides, and forms.