Ethics in NLP

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Related lectures (30)

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Ethical Considerations in Natural Language Processing

Explores ethical challenges in NLP systems, including biases, toxicity, privacy, and disinformation.

Ethics in Natural Language Processing: Addressing Bias and Misinformation

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

Modern NLP and Ethics in NLP

Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.

Pretraining Sequence-to-Sequence Models: BART and T5

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

Ethics and Fairness in Machine Learning

Explores the ethical implications of deploying machine learning algorithms and emphasizes the importance of fairness in decision-making processes.

Bias in Machine Learning

Delves into the impact of bias in machine learning models and the importance of evaluating potential harm in developing such systems.

Data Annotation: Collection and Biases in NLP

Addresses data collection, annotation processes, and biases in natural language processing.

Ethics and Law of AI

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

Introduction to Modern Natural Language Processing

Introduces the course on Modern Natural Language Processing, covering its significance, applications, challenges, and advancements in technology.

Natural Language Processing: Understanding Transformers and Tokenization

Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.