Yes, embeddings can be evaluated for fairness, especially when there is concern about biases in the representation of different groups or characteristics in the data. Evaluating fairness in embeddings involves detecting and mitigating bias, such as gender, racial, or ethnic biases, which can emerge during model training.
One method for evaluating fairness in embeddings is through fairness metrics, which measure the degree to which certain sensitive attributes (e.g., gender, race, etc.) are unfairly correlated with other attributes (e.g., occupation, sentiment, etc.). For example, in word embeddings, metrics like Word Embedding Association Test (WEAT) can be used to measure how biased associations between words or concepts are.
Fairness evaluation also involves testing whether embeddings lead to equitable outcomes in downstream tasks. If an embedding model consistently produces biased results (e.g., discriminating against certain groups in job recommendation systems), it may indicate fairness issues that need to be addressed. Techniques like debiasing or using more representative training data can help improve the fairness of embeddings.