Engineering Ethics Education: Methods and Effectiveness

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Delves into ethics and law in AI, focusing on values, normative ambitions, and ethical challenges.

Explores ethical reflections on human good, moral standards, and the importance of collaboration for a good life.

Explores consequentialism, deontology, and virtue ethics, highlighting their strengths and challenges in making ethical decisions.

Discusses the ethical implications of artificial intelligence for engineers and its regulatory context.

Explores ethical argumentation, emphasizing the duty to justify actions and the hierarchy of goods at stake in ethical dilemmas.

Explores the Ethics and Law of AI, focusing on data ethics, bias, and social justice in AI systems.

Explores the Ethics of AI, focusing on Corporate Digital Responsibility and the role of government in regulating AI initiatives.

Explores the desire for a good life with and for others, discussing the origins of ethics, the common good, and the ethical problem of partiality.

Explores data ethics, focusing on fairness, justice, and Al systems' broader implications.

Guides students through reflecting on applying the ethical analysis cycle and understanding ethics education goals.