💡 Overview
AI models like BERT, GPT, and image classifiers learn from massive text and image datasets. However, if that data carries societal biases, the models replicate them. Recognizing this is the first step toward fairness.
⚙️ How Bias Enters Models
- Imbalanced datasets — overrepresentation of certain groups or terms.
- Historical prejudice — language data encoding stereotypes.
- Annotation bias — human labellers injecting opinion or error.
🧩 Example: Gender Bias in Word Embeddings
Observation: When trained on raw text, word embeddings might associate “doctor” with “male” and “nurse” with “female.”
Solution: Use de-biasing algorithms such as *Hard Debiasing* to neutralize gender associations.
Solution: Use de-biasing algorithms such as *Hard Debiasing* to neutralize gender associations.
# Example in Python using WEAT (Word Embedding Association Test)
from wefe.metrics import WEAT
from wefe.wordembedding import WordEmbeddingModel
model = WordEmbeddingModel("glove.6B.300d.txt", "glove")
weat = WEAT()
result = weat.run_query(
["doctor", "engineer"], ["nurse", "teacher"],
["man", "male"], ["woman", "female"], model
)
print(result)
✅ CTO Takeaway
Bias isn’t always visible, but its impact is real. Implement bias audits before deployment and regularly re-train with diverse, representative data.