Even if data is clean and neatly converted into tensors, models can still learn false patterns if the underlying data is flawed. Here's how to mitigate that risk:
- Check distributions: Look for imbalances in class labels, demographics, etc.
- Verify labels: Ensure labeling quality — are there systematic labeling errors or biases?
- Detect outliers: Use clustering or statistical tools to find anomalies.
- Counterfactual testing: Would the model behave the same if a sensitive attribute (e.g. gender, race) were changed?
- Bias metrics: Use tools like:
- IBM AI Fairness 360
- Google What-If Tool
- Expert review: Bring in domain experts to validate critical data slices.
- Red-teaming: Involve diverse stakeholders to stress-test assumptions.
- Debiasing algorithms: Reweight, resample, or apply adversarial debiasing.
- Data augmentation: Add synthetic or balanced samples to improve generalization.
- Regularization: Prevent overfitting on biased regions of the data.
- Interpretable models: Use tools like SHAP, LIME, or attention maps to understand what the model is focusing on.
- Challenge sets: Curated examples to stress-test model behavior on edge cases.
- Saliency maps: Visualize what inputs drive the model’s decisions.
Turning data into tensors is just a mechanical step. Ensuring data quality, fairness, and representativeness is a continuous, interdisciplinary process that combines statistics, ethics, and domain knowledge.