Research Ethics

Learning time distribution

Learning outcomes

Knowledge

• Students should obtain basic knowledge of the theoretical and historical foundations of ethics.
• They should learn to identify and explain key ethical challenges in computational design with special emphasis on AI development and deployment and data usage, identify ethical risks, and critically evaluate AI’s impact on society, autonomy and decision-making.
• They should gain knowledge and understanding of the applicability of ethical principles, values, and rules to situations in the field of informatics by identifying, analyzing and solving problems at the confluence of these fields.

Skills

• The ability to transfer acquired knowledge and apply it in a creative-innovative way to solve complex problems raised by the activity of creating and/or managing AI.
• The ability to design computational systems with ethical and regulatory compliance in mind.
• The ability to apply the principles and values of ethics and integrity in the area of data management, and in a wider research context.

Responsibility

• An attitude of solidarity with the beneficiaries of specific products and colleagues in teamwork
• Respect for national as well as European/international values and ethical standards
• Tolerance and respect for diversity.

Online platform

Course content

Course bibliography

Aristotle (2009). Nicomachean ethics (W. D. Ross, Trans.; Revised ed.). Oxford University Press. (Original work published ca. 4th century BCE) Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT Press. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (pp. 149–159). Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press. Floridi, L. (2016). The Fourth Revolution: How the Infosphere is Reshaping Human Reality. Oxford University Press. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review Jonas, H. (1984). The imperative of responsibility: In search of an ethics for the technological age. University of Chicago Press. Kant, I. (2012). Groundwork of the metaphysics of morals (M. Gregor & J. Timmermann, Trans.). Cambridge University Press. (Original work published 1785) Mill, J. S. (2002). Utilitarianism (G. Sher, Ed.). Hackett. (Original work published 1861) O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown. Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Viking. Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. (2019). Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems (1st ed.). IEEE. https://ethicsinaction.ieee.org European Commission. (2019). Ethics guidelines for trustworthy AI. High-Level Expert Group on Artificial Intelligence. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000381137

Seminar content

Seminar bibliography

As specified in the curriculum

Corroboration

(none)

AI tools guidance

Evaluation and delivery

Performance standards

Achieving 50% of the maximum number of points.

Additional info

(none)