How do I evaluate whether feature attribution indicates hidden biases?

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How do I evaluate whether feature attribution indicates hidden biases?

Asked on Oct 15, 2025

Answer

Evaluating feature attribution for hidden biases involves analyzing how model explanations, such as SHAP or LIME, highlight the importance of features and whether these attributions reveal any unfair or biased patterns. By examining these attributions, you can identify if certain features disproportionately influence model predictions in a way that could lead to biased outcomes.

Example Concept: Feature attribution methods like SHAP and LIME provide insights into which features most influence model predictions. By reviewing these attributions, you can detect potential biases if certain features, especially those related to sensitive attributes (e.g., race, gender), have an outsized impact. This analysis helps ensure that the model's decision-making process is fair and transparent.

Additional Comment:

Use SHAP or LIME to generate feature attributions for your model.
Analyze the attributions for any sensitive features to see if they have a disproportionate impact on predictions.
Consider retraining the model with bias mitigation techniques if hidden biases are detected.
Document findings and actions taken in a model card for transparency.

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