DIGITAL HUMANITIES 3: DIGITAL ETHICS

This course deals with digital technologies and the associated ethical and social implications from a multidisciplinary perspective. The overall objectives of the course are 1) to introduce the main societal and ethical issues connected to the development and deployment of digital tools and, in particular, big data enabling technologies, such as those developed in the context of machine learning and data science; 2) to gain a better understanding of the variety of factors involved in the ethical and social issues of digital technologies (technical, philosophical, business, legal factors, etc.); 3) to offer conceptual tools to critically analyze the impact of digital applications on individuals and the society as a whole.

Knowledge and understanding
The mechanisms enabling the exploitation of big data and in particular machine learning.
Ethical and social problems arising in the context of digital technologies and big data (such as privacy, algorithmic fairness and transparency)
Digital technologies as socio-technical systems

Capability of applying knowledge and comprehension
To apply the learned notions appropriately to concrete scenarios.
To become familiar with the terminology and approaches offered by different fields of study (computer science, philosophy, law, etc).
To distinguish the factors involved in ethical problems connected to the application of big data and digital technologies.
To identify and critically analyze the ethical and social problems in specific use cases.


Capability of judgment
To elaborate a critical reflection and arguments with respect to the adoption and application of digital technologies in the contemporary world.

Communication skills
To apply the learned terminology appropriately.
To clearly express critical reflections and arguments in written and oral text.
To interact with peers and the instructor in a constructive way.

None.

Ethics in the context of digital technologies
Computational systems and socio-technical systems
Big data, artificial intelligence, machine learning and data science
Privacy and data protection
Algorithmic fairness, bias and discrimination
Transparency and accountability

Articles and book chapters that will be proposed during the course

An intermediate assignment to be performed during the lectures and a final group presentation.

Frontal lessons and group discussions.

oral

This subject deals with topics related to the macro-area "Poverty and inequalities" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development