ECTS - University of Pisa
The digital economy and the digital society harness the power of big data, computational capacity, innovation and interconnection. Every human activity is mediated by information technologies. Today’s technologies enable unprecedented exploitation of information, being it small or big data, for any thinkable purpose, but mostly in business and surveillance with the ensuing legal and ethical anxieties and constraints.
Algorithms are regularly used for mining data, offering unexplored patterns and deep non-causal analyses to those businesses able to exploit these advances. Yet, these innovations need to be properly framed in the existing legal background, fit in the existing set of guarantees of fundamental rights and freedoms, coherently policy related to reap the richness of big and open data and administration while empowering equally all players. For these aims data protection plays a significant role.
At the same time, artificial intelligence agents operate on big data corpora that are made of information, personal data and other materials that may or may not be protected by exclusive rights. Ownership and data governance issues are daily arising. Legal regimes are the most various, and often not similar across States. Recently, national and supranational legislators have flanked data protection laws with non-personal data protection laws, mostly oriented to strike a balance between, on the one hand, protection of and incentives for data producers and, on the other hand, the need for a free flow of such data to support further innovation.
The course aims at enabling students to work on algorithms and data mining techniques in ways that are compliant to the applicable legal framework and aware of the interplay between techniques and normative rules.
The digital economy and the digital society harness the power of big data, computational capacity, innovation and interconnection. Every human activity is mediated by information technologies. Today’s technologies enable unprecedented exploitation of information, being it small or big data, for any thinkable purpose, but mostly in business and surveillance with the ensuing legal and ethical anxieties and constraints.
Algorithms are regularly used for mining data, offering unexplored patterns and deep non-causal analyses to those businesses able to exploit these advances. Yet, these innovations need to be properly framed in the existing legal background, fit in the existing set of guarantees of fundamental rights and freedoms, coherently policy related to reap the richness of big and open data and administration while empowering equally all players. For these aims data protection plays a significant role.
At the same time, artificial intelligence agents operate on big data corpora that are made of information, personal data and other materials that may or may not be protected by exclusive rights. Ownership and data governance issues are daily arising. Legal regimes are the most various, and often not similar across States. Recently, national and supranational legislators have flanked data protection laws with non-personal data protection laws, mostly oriented to strike a balance between, on the one hand, protection of and incentives for data producers and, on the other hand, the need for a free flow of such data to support further innovation.
The course aims at enabling students to work on algorithms and data mining techniques in ways that are compliant to the applicable legal framework and aware of the interplay between techniques and normative rules.
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- The Algorithmic Society: the Classifying Society – Background and Overview, Surveillance Society – Big Other, Networks of Control, Predicting Behavior, People Analytics, Behavioural “Nudging”, New Emerging Human Rights in the age of Behavioral Data Science and Neurotechnologies: Towards "Mental Privacy" and "Decision Integrity", Legal and ethical implication of computational capacity.
-Building Legally-Compliant Algorithms: Legal Pitfalls of Algorithms, The Problems of Personalization, Data Handling & Sharing, Deploying Algorithms for Human Rights—Complications & Challenges, Classification of Algorithms in the Information Society: Legal Implications and Business Applications, Exploitation of Public Sector Data, Competition Law in the Age of Algorithms, Transparency, accountability and traceability of algorithm based decision-making, Accountability in the Machine Learning Context, Technical and Legal Options to Enhance Transparency & Accountability, Legal Liability for Algorithm Autocomplete (ISP Liability), Open Data Governance, Data Ethics.
- The General Data Protection Regulation: Notions and principles, GDPR global reach and compliance, extra-eu data flows.
- Privacy in operation: Privacy-by-Design, GDPR Solutions: The Right to an Explanation, etc. Notions of Privacy in the Algorithmic Age, Privacy from the Government, Surveillance Capitalism, Governance by Proxy, Privacy from Private Entities, Privacy from Platforms, Privacy from Employers, Privacy from our Devices (IoT).
- Comparative Perspectives & Crossborder Issues: Data management for tailored purposes (healthcare, R&D&I, statistics etc.)
- Data ownership and data governance between personal and non-personal data: a comparative analysis. Closeness vs openness of data corpora. The legal regime of public-sector information (PSI Directive) and of specific non-personal data.
- The Algorithmic Society: the Classifying Society – Background and Overview, Surveillance Society – Big Other, Networks of Control, Predicting Behavior, People Analytics, Behavioural “Nudging”, New Emerging Human Rights in the age of Behavioral Data Science and Neurotechnologies: Towards "Mental Privacy" and "Decision Integrity", Legal and ethical implication of computational capacity.
-Building Legally-Compliant Algorithms: Legal Pitfalls of Algorithms, The Problems of Personalization, Data Handling & Sharing, Deploying Algorithms for Human Rights—Complications & Challenges, Classification of Algorithms in the Information Society: Legal Implications and Business Applications, Exploitation of Public Sector Data, Competition Law in the Age of Algorithms, Transparency, accountability and traceability of algorithm based decision-making, Accountability in the Machine Learning Context, Technical and Legal Options to Enhance Transparency & Accountability, Legal Liability for Algorithm Autocomplete (ISP Liability), Open Data Governance, Data Ethics.
- The General Data Protection Regulation: Notions and principles, GDPR global reach and compliance, extra-eu data flows.
- Privacy in operation: Privacy-by-Design, GDPR Solutions: The Right to an Explanation, etc. Notions of Privacy in the Algorithmic Age, Privacy from the Government, Surveillance Capitalism, Governance by Proxy, Privacy from Private Entities, Privacy from Platforms, Privacy from Employers, Privacy from our Devices (IoT).
- Comparative Perspectives & Crossborder Issues: Data management for tailored purposes (healthcare, R&D&I, statistics etc.)
- Data ownership and data governance between personal and non-personal data: a comparative analysis. Closeness vs openness of data corpora. The legal regime of public-sector information (PSI Directive) and of specific non-personal data.
Materials will be distributed in class and distributed on demand: giovanni.comande@santannapisa.it; Denise Amram denise.amram@santannapisa.it
Materials will be distributed in class and distributed on demand: giovanni.comande@santannapisa.it; Denise Amram denise.amram@santannapisa.it
Please contact professors denise.amram@santannapisa.it giovanni.comande@santannapisa.it
Please contact professors denise.amram@santannapisa.it giovanni.comande@santannapisa.it
Written test with open question. Oral exam in case of refusals / failure.
It is not possible to pass the test if the candidate shows an inability to express him/herself in a clear manner using the correct terminology, or if the candidate does not respond sufficiently to questions regarding the most fundamental part of the course. The test will not have a positive outcome if the candidate repeatedly demonstrates an incapacity to relate and link parts of the programme with notions and ideas that they must combine in order to correctly respond to a question.
Written test with open question. Oral exam in case of refusals / failure.
It is not possible to pass the test if the candidate shows an inability to express him/herself in a clear manner using the correct terminology, or if the candidate does not respond sufficiently to questions regarding the most fundamental part of the course. The test will not have a positive outcome if the candidate repeatedly demonstrates an incapacity to relate and link parts of the programme with notions and ideas that they must combine in order to correctly respond to a question.
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