AI Ethics: Data Bias and Justice

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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.

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

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

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

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

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

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

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

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