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PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

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Data Ethics and Data Protection

01URZSM

A.A. 2024/25

Course Language

Inglese

Degree programme(s)

Master of science-level of the Bologna process in Data Science And Engineering - Torino

Course structure
Teaching Hours
Lezioni 48
Esercitazioni in aula 12
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
De Martin Juan Carlos Professore Ordinario IINF-05/A 18 12 0 0 2
Co-lectures
Espandi
Context
SSD CFU Activities Area context
ING-INF/05
IUS/01		 3
3		 B - Caratterizzanti
F - Altre attività (art. 10)		 Ingegneria informatica
Altre conoscenze utili per l'inserimento nel mondo del lavoro
Statistiche superamento esami
2024/25
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.
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, addressing the most relevant issues in the ongoing debate on law & ethics with regard to data-intensive systems. In this context, data protection and AI regulations will be discussed, including risk assessment methodologies. The course then deals with the impact of data use and AI on fundamental rights and ethical values, considering different proposals and guidelines on data ethics and socio-ethical impact management. The second part of the course is focused on the computer science perspective, with a focus on data-driven automated decisions making (ADM) systems. Students will analyse and interactively discuss in class selected case studies in relation to ethical issues raised by the deployment of ADM systems. Special attention will be devoted to the systematic discriminations and issues of inequalities. The reference point of such discussions will be the ACM Code of Ethics and Professional, necessary to understand the ethical and professional duties of data scientists and software practitioners. Students will also learn basic concepts and methodologies to design a risk assessment for ADM systems, evaluating the possible negative impacts of ADM systems for individuals, vulnerable groups and society in general.
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.
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 evaluate risks of systematic discriminations in the deployment of data-driven ADM systems.
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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
• Data-driven technology and society • Law & technology • Data protection regulation (GDPR) • AI regulation • Risk assessment and human rights impact assessment • Ethical and data policies and guidelines • Socio-ethical impact assessment in AI • Fundamental concepts in data ethics • Case studies on data ethics and policy issues • Bias in software systems and data • Statistical formalizations of algorithmic fairness • ACM Code of Ethics and Professional
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The course is divided into lectures, students' contributions and group activities will be encouraged
The course is divided into lectures and class exercises, students' contributions and group activities will be encouraged, also leveraging innovative pedagogical methodologies, including case studies, and according to the principles of active learning.
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.
Given the multidisciplinary nature of this course and the evolving regulatory and technology scenario in the field of data protection and data ethics, the complete 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). This is the list of the main materials: • Mantelero, A. (2022) Beyond Data. Human Rights, Ethical and Social Impact Assessment in AI. Springer-Asser, open access • ACM. 2018. Code of Ethics and Professional Conduct https://www.acm.org/code-of-ethics • Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. Limitations and Opportunities (2019). Available online on https://fairmlbook. org/ Supplementary readings: • 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.
Slides; Esercizi risolti;
Lecture slides; Exercise with solutions ;
Modalità di esame: Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Computer-based written test in class using POLITO platform;
... 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. Given the multidisciplinary nature of this course and its expected learning outcomes in both computer science and law, students are required to achieve the sufficiency in at least three questions (sufficiency threshold for each question is 4,5 points). It is not allowed to communicate with others during the exam, use a phone, and keep and consult notebooks, books, slides, and forms.
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Computer-based written test in class using POLITO platform;
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 topics (40 minutes) and one on computer science topics (50 minutes). The students should address two questions for each of these sections; the questions may be case studies and also include multiple-choice answering. Each question worth 7,5 points maximum. Given the multidisciplinary nature of this course and its expected learning outcomes in both computer science and law, at least three questions must be passed (the threshold for each question is 4,5 points). It is not allowed to communicate with others during the exam, use a phone, and keep and consult notebooks, books, slides, and forms.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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