B0708 - COMPUTATIONAL ETHICS

Learning outcomes

At the end of the course, the student is familiar with the main meta-ethical issues, normative ethical models, and theoretical contributions of cognitive science related to the translatability of moral judgments in computational terms. At the end of the course, the student is able to critically reason about alternative theoretical proposals for algorithmic formulation of ethics, as well as about theories that deny the possibility of computational ethics.

Course contents

The course aims to provide

• an introduction to computational ethics;
• an introduction, from a critical perspective, to the ethics of artificial intelligence.

There are no prerequisites. The course is designed for students who have never studied philosophy.

Readings/Bibliography

Useful materials will be made available to students.

 

Recommended readings:

• Kate Crawford, Atlas of AI. Power, Politics, and the Planetary Costs of Artificial Intelligence, New Haven and London, Yale University Press, 2021.

• N. Cristianini, Shortcuts to Artificial Intelligence, in Machines We Trust. Perspectives on Dependable AI, ed. by M. Pelillo, T. Scantamburlo, Cambridge, Massachusetts, The MIT Press, 2021, pp. 11-25.

• D. Greene, A.L. Hoffman, L. Stark, Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning, 10. Hawaii International Conference on System Sciences (HICSS), 2019, pp. 2122-2131.

Teaching methods

Lectures and class discussions.

Assessment methods

Final oral exam.
Continuous assessment.

Teaching tools

Useful materials will be made available to students.

Office hours

See the website of Daniela Tafani