How can I improve the explainability of my Deep Learning models for stakeholders?

All answers to this question.

In the healthcare space, look into "Integrated Gradients" or "Attention Mapping" if you are using Transformers. These techniques show exactly which parts of the input data (like specific pixels in an X-ray or words in a patient report) contributed most to the final decision. Another emerging trend in 2025 is "Counterfactual Explanations," which tell the stakeholder: "If this specific lab value had been X instead of Y, the prediction would have changed from Positive to Negative." This is much more intuitive for doctors than a abstract SHAP value and helps build much-needed clinical trust.

   Answered 2025-05-14 by Patricia Moore

Are you using inherently interpretable models like "Explainable Boosting Machines" (EBMs) as a baseline to compare against your deep learning model's performance?

   Answered 2025-05-20 by Gregory Taylor

• Gregory, we actually started with EBMs! They were very easy to explain, but we saw a 15% drop in accuracy compared to our CNN-based approach. The medical board wants the accuracy of the deep learning model but the transparency of the EBM. It’s a tough balance. We are now looking at "Knowledge Distillation" where we use the deep learning model to train a smaller, more interpretable decision tree that can act as a "proxy" for the complex network.

     Commented 2025-05-25 by Kevin Scott

Don't underestimate the power of a good UI. Sometimes a visual heat map on an image is all a doctor needs to verify the model is looking at the right area.

   Answered 2025-06-01 by Linda White

• I agree with Linda. Visualizing the "Attention" heads in a transformer can be a very powerful way to show stakeholders that the model is focusing on relevant clinical markers.

     Commented 2025-06-03 by Sarah Jenkins