Bengali sentiment analysis models exhibit persistent identity-based biases across datasets and developer backgrounds despite similar semantic content.
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DECAF synthetic data generator best balances privacy and fairness while fairness pre-processing improves outcomes more on synthetic data than real data, though at some cost to predictive accuracy.
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How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Language
Bengali sentiment analysis models exhibit persistent identity-based biases across datasets and developer backgrounds despite similar semantic content.
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Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms
DECAF synthetic data generator best balances privacy and fairness while fairness pre-processing improves outcomes more on synthetic data than real data, though at some cost to predictive accuracy.