Captum

Step 1: Load the image and the pre-trained image classification model.

Step 2: Run the image through the model and observe the predicted class.

Step 3: Use Integrated Gradients to attribute the prediction to different regions of the image.

Step 4: Highlight the regions with the highest attribution scores to identify the areas the model is focusing on.

Step 5: Analyze if the highlighted regions align with the actual object in the image to diagnose the misclassification.

Step 1: Load the text and the pre-trained sentiment classification model.

Step 2: Tokenize the text and convert it into a numerical representation.

Step 3: Run the tokenized text through the model and observe the predicted sentiment.

Step 4: Use Integrated Gradients to attribute the prediction to each token in the text.

Step 5: Highlight the tokens with the highest attribution scores to identify the key words driving the sentiment prediction.

Step 1: Load the transaction data and the pre-trained fraud detection model.

Step 2: Normalize and prepare the feature data for the model.

Step 3: Run the transaction data through the model and observe the fraud score.

Step 4: Use Integrated Gradients to attribute the fraud score to each feature in the transaction data.

Step 5: Analyze the feature attributions to determine which features contribute the most to the prediction of fraud.

Step 1: Load the patient data and the pre-trained medical diagnosis model.

Step 2: Preprocess the patient data according to the model's requirements.

Step 3: Run the data through the model and observe the predicted diagnosis.

Step 4: Use Layer Attribution to determine the influence of different layers on the diagnosis.

Step 5: Analyze the layer attributions to ensure that the diagnosis is based on relevant medical factors.

Step 1: Load the data and the model.

Step 2: Divide the data into groups based on demographic attributes (e.g., gender, race).

Step 3: For each group, calculate feature attributions using Integrated Gradients or other methods.

Step 4: Compare the feature attributions across groups to identify significant differences.

Step 5: Investigate the reasons for the differences and address any biases found in the model or data.