Lectures related to AI Ethics: Data Bias and Justice

AI Ethics: Bias, Responsibility, and Accountability

Explores AI ethics, addressing bias, responsibility, and accountability in algorithm design and outcomes.

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

Deep Learning: Data, Models, and Challenges

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Fairness in Algorithmic Decision-Making

Delves into the challenges of ensuring fairness in algorithmic decision-making processes, addressing biases and historical injustices in data.

Global Data Justice: Social Justice Approach to Data Governance

Explores global data justice, social fairness in data governance, algorithmic ethics, and the political responsibility of researchers.

Ethics in Natural Language Processing: Addressing Bias and Misinformation

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

Ethics in Technology: Understanding AI Implications

Covers the ethical implications of artificial intelligence in technology, focusing on data, algorithms, design, sustainability, and accountability.

AI Ethics: Legal Framework

Explores the legal framework of AI ethics, including the interpretation of laws, sources of legal norms, and the prohibition of harmful AI practices.

Ethical Issues in AI

Explores the ethical challenges of artificial intelligence, including explainability, security, singularity, and societal impact.

Ethics and Law of AI: Feedback and Challenges

Discusses student feedback, challenges in balancing laws and ethics, and the need for more critical thinking in AI ethics.

Ethics in Artificial Intelligence: Key Considerations

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

AI Ethics and Justice

Explores the ethical implications of AI on justice and fairness in society, emphasizing the importance of considering social aspects in decision-making.

Understanding Intelligence and AI Ethics

Explores the definition of intelligence in AI and humans, the role of communication and emotion, and ethical considerations in AI regulation.

Ethics and Law of AI

Covers the Ethics and Law of AI, addressing relevant ethical and legal questions and providing insights on AI regulation.

The Governance of Infrastructure

Explores governance challenges, efficiency improvement, stakeholder engagement, and corruption impact in infrastructure projects.

Generative AI and Reinforcement Learning: Future Directions

Explores advancements in generative AI and reinforcement learning, focusing on their applications, safety, and future research directions.

AI Ethics: EU Regulations Overview

Explores the EU regulations on AI ethics, emphasizing the AI Act's impact on internal markets, fundamental rights, and fair competition.

Ethics of AI: Data, Algorithms, and Integration

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

AI Ethics: Trustworthy AI and Ethical Principles

Discusses trustworthy AI, ethical principles, and US AI regulation.

Telecommunication Infrastructure and Smart Cities: How Digitalization Impacts City Governance

Delves into telecommunication infrastructure, smart cities, governance, sustainability, challenges, and real-world impacts of digitalization.