Ethics in NLP

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Related lectures (30)

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Deep Learning: Principles and Applications

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

Multilingual NLP: Challenges and Innovations

Covers the importance of multilingual NLP and the challenges in scaling language models.

Introduction to Human Rights

Introduces key human rights concepts, explores discrimination, social norms, and the impact of algorithms on rights.

Faster Gaze Prediction: Dense Networks and Fisher Pruning

Explores the development of faster architectures for saliency prediction using deep networks and pruning methods.

Natural Language Processing: A Primer

Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.

Classical Language Models: Foundations and Applications

Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.

Natural Language Generation

Explores Natural Language Generation, covering neural models, biases, ethics, and evaluation challenges.

Modern NLP: Introduction

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Anonymity and Accountability

Explores the significance of anonymity in avoiding judgment based on personal characteristics and the need to balance it with accountability.

Transformers: Pretraining and Decoding Techniques

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.