Detecting bias in embeddings involves evaluating how embeddings reflect various demographic or societal biases, such as gender, race, or age. One common method is to examine the relationships between different types of words or items in the embedding space. For example, in word embeddings, biased associations may appear if words like "nurse" are closer to "female" and "doctor" is closer to "male." Researchers and developers can use probes or specific tasks to identify these biases by examining if certain groups or attributes are disproportionately represented or misrepresented in the embedding space.
Techniques like the "Word Embedding Association Test" (WEAT) are used to measure bias by comparing how different groups are associated with positive or negative attributes in the embedding space. For instance, WEAT can be used to assess whether certain professions are biased towards particular genders or ethnicities. Another approach is to visualize embeddings using dimensionality reduction methods like t-SNE to spot biased clusters or outliers.
Once bias is detected, techniques like debiasing or retraining the embedding model with more balanced data can help mitigate these issues. Debiasing methods aim to adjust the embeddings to reduce unfair correlations between sensitive attributes and other aspects of the data, promoting fairness and neutrality in the embeddings.