Evaluating Word Embeddings and Bias in Natural Language Processing

Natural language processing powers the tools we use daily, but the mathematical representations of language underneath often harbor deep systemic biases. To build ethical and reliable text-based applications, you must understand how these models see the world. This course introduces you to the core mechanics of word embeddings and provides you with the foundational knowledge to spot and address bias in language technology. You will transition from understanding basic text representation to confidently evaluating modern language models for fairness. By reading through clear explanations and studying concrete code examples, you will learn how to analyze vector spaces and implement mitigation strategies. What you'll learn: - Understand the foundational concepts of vector space models and how words are translated into numerical representations - Compare static embedding techniques like GloVe with contextualized representations from transformer-based models like BERT - Identify how societal biases and stereotypes become encoded in high-dimensional vector spaces - Measure bias using standard evaluation metrics and similarity calculations - Apply modern debiasing techniques to neutralize harmful associations in text representations - Evaluate ethical considerations and fairness trade-offs in downstream NLP applications We begin with essential terminology, exploring how machines process text and map meaning. From there, you will progress to studying specific embedding architectures, analyzing how bias creeps into training data, and exploring modern methods to measure and mitigate these issues. This course is designed for beginners, software developers, and data enthusiasts who want to understand the ethical dimensions of NLP. No prior background in advanced machine learning or high-level mathematics is required. Start reading today to build more equitable and responsible language technology.