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Related lectures (31)

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Ethics and Law of AI: Reflections and Challenges

Reflects on the challenges of defining intelligence and self-consciousness in the context of AI, exploring ethical implications and the boundaries of artificial entities in society.

Introduction to Ethics: Desire, Good Life, and Moral Reflection

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.

Ethics in Natural Language Processing: Addressing Bias and Misinformation

Covers ethical considerations in NLP, focusing on bias, toxicity, and misinformation.

Ethics of AI: Data, Algorithms, and Integration

Explores the ethical challenges of AI, including data ethics, algorithm ethics, and societal impact.

Ethical Analysis Cycle Reflection

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

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Ethical Analysis Cycle: Major Normative Theories and Engineering Design

Explores major ethical theories and an engineering design approach to solve moral problems effectively.

Understanding Autonomy

Explores the challenges and distinctions between human and artificial autonomy, touching on ethical implications and the conditions required for true autonomy.

Designing Learning Tools: Introduction

Introduces 'How People Learn: Designing Learning Tools' at EPFL, focusing on emotions, diversity, and ethics in engineering education.

Ethics in Artificial Intelligence: Key Considerations

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