From the course: Introduction to NLP and LLMs: Principles and Practical Applications
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Bias and fairness in NLP and LLMs
From the course: Introduction to NLP and LLMs: Principles and Practical Applications
Bias and fairness in NLP and LLMs
- [Instructor] Understanding bias and fairness in NLP and LLMs is crucial for responsible AI development and deployment. Bias in AI occurs when models systematically favor or this favor specific groups or outcomes due to flawed training data or model design. In NLP and LLMs, this can manifest as the reflection or amplification of societal stereotypes, making addressing bias crucial for equitable systems. Bias in NLP and LLMs often stems from two sources. Data imbalances are unequal representation of different groups in the training and preexisting bias or societal stereotypes and biases reflected in the training data. For example, a chatbot giving different responses to identical queries based on user demographic differences demonstrates biased behavior. In this scenario, you'll see how a chatbot might handle queries differently based on language preference. A customer service chatbot for a global airline receives the query "What is the baggage policy?" A user asking in English…
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