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Related Questions
- Can saliency maps reveal hidden patterns or correlations in text data that may lead to biases in summarization models?
- How can saliency maps be used to identify biases in summarization models that are intended to be neutral or impartial?
- Can machine learning interpretability techniques like saliency maps uncover potential biases in text representations that may affect summarization model outputs?
- Do saliency maps have the capability to pinpoint areas in the text that are driving biased summarizations, even if the model's architecture is not inherently biased?
- Can the analysis of saliency maps help developers identify potential sources of bias in data, such as imbalanced or noisy training data?
- How can saliency maps be used to evaluate the fairness of summarization models in relation to sensitive attributes like demographic characteristics or personal preferences?
- Can saliency maps be used to compare the performance of different summarization models on diverse datasets and identify potential biases that may arise from the training data?
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