Learning Objectives
Identify how conceptual metaphors in training data embed and propagate social biases into AI models
Apply prototype theory to analyze systematic classification bias patterns in AI output
Use frame analysis as a rigorous method for detecting and measuring bias in LLM outputs
Design comprehensive bias detection workflows grounded in cognitive-linguistic methodology
How cognitive-linguistic structures systematically encode social biases in AI systems — and how to detect them.
This lesson builds on the theoretical foundations established earlier in the module and extends your understanding of how cognitive-linguistic principles apply directly to modern AI systems.
Learning Objectives
Identify how conceptual metaphors in training data embed and propagate social biases into AI models
Apply prototype theory to analyze systematic classification bias patterns in AI output
Use frame analysis as a rigorous method for detecting and measuring bias in LLM outputs
Design comprehensive bias detection workflows grounded in cognitive-linguistic methodology
Key Concepts
Identify how conceptual metaphors in training data embed and propagate social biases into AI models
Apply prototype theory to analyze systematic classification bias patterns in AI output
Use frame analysis as a rigorous method for detecting and measuring bias in LLM outputs
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