Explainability AI Prompts

Generate counterfactual explanations for rejected or unfavorable predictions from this model. A counterfactual answers the question: 'What is the minimal change to the input that would flip the prediction?' For the top 10 most impactful negative predictions (e.g. loan rejected, churn predicted, fraud flagged): 1. Find the nearest counterfactual: the smallest change to input features that would result in a positive prediction 2. Constraints: only change features that are actionable (not age, not historical data — only things the person can change) 3. For each counterfactual show: original values | counterfactual values | what changed | magnitude of change 4. Rank the required changes from easiest to hardest to achieve 5. Generate a plain-English 'what you could do differently' explanation for each case Return: counterfactual table for each case and template text suitable for a customer-facing explanation.

Build a simple decision tree that approximates the behavior of this complex model. 1. Generate predictions from the complex model on the full training set 2. Train a decision tree on those predictions (use model outputs as the new target) 3. Limit the tree depth to 4 levels maximum for interpretability 4. Tune: find the depth (1–6) that maximizes fidelity (agreement with the complex model) while staying interpretable 5. Visualize the decision tree using graphviz or a text representation 6. Extract the top 5 decision rules as plain-English if-then statements 7. Report fidelity: what percentage of predictions does the proxy tree agree with the complex model? Note: this is a surrogate model, not the real model. Flag where the proxy disagrees most with the original.

Explain which features matter most to this model. 1. Extract built-in feature importances from the model (gain, split count, or permutation importance) 2. Plot a horizontal bar chart of the top 20 features, ranked by importance 3. Compute permutation importance on the validation set as a cross-check — compare to built-in importances 4. Flag any features where built-in and permutation importances disagree significantly 5. Identify features with near-zero importance in both methods — candidates for removal 6. Group features by type (original vs engineered) and show which group contributes more total importance Return: importance table, bar chart, and a one-paragraph plain-English explanation of what the model is learning.

Step 1: Global importance — compute and plot SHAP feature importances (beeswarm). Identify the top 5 features driving predictions. Step 2: Effect direction — create SHAP dependence plots for the top 5 features. Describe the relationship between each feature and the prediction (linear, threshold, non-linear). Step 3: Interaction analysis — compute SHAP interaction values. Identify the strongest pairwise interaction and plot it as a 2D PDP. Step 4: Local explanation — generate waterfall plots for 3 representative predictions: high, low, and borderline. Step 5: Business translation — write a 1-page non-technical explanation of how the model makes decisions, using analogies and avoiding all technical terms. Step 6: Risk flagging — identify any feature effects that seem counterintuitive or potentially problematic from a fairness or business logic perspective.

Use LIME to explain individual predictions from this model in plain English. Generate LIME explanations for 5 specific predictions: 1. One very high prediction (top 5% of predicted values) 2. One very low prediction (bottom 5% of predicted values) 3. One borderline prediction (near the decision threshold) 4. The single prediction the model got most wrong 5. A randomly selected typical prediction For each explanation: - Show the top 10 features that pushed the prediction up or down - Display as a horizontal bar chart with green bars (positive contribution) and red bars (negative contribution) - Write a 2-sentence plain-English explanation: 'The model predicted [value] primarily because [top driver]. This was offset by [top negative driver].' Return all 5 explanations with plots and text summaries.

Write a complete model behavior report suitable for a technical stakeholder review. The report should cover: 1. What the model learned — top 10 features and their direction of effect, in plain English 2. Decision rules — extract the top 5 decision paths from the model using SHAP or tree rules 3. Edge cases — what input combinations lead to extreme predictions (very high and very low)? 4. Monotonicity check — for features where a directional relationship is expected (e.g. more experience → higher salary), does the model respect that direction? 5. Interaction effects — which two features interact the most strongly? How does their interaction affect predictions? 6. Sensitivity analysis — which single feature, if changed by 10%, has the largest average impact on predictions? Format as a structured report with section headings, plots, and a non-technical executive summary at the top.

Generate partial dependence plots (PDPs) and individual conditional expectation (ICE) plots for the top features in this model. For each of the top 5 most important features: 1. Plot the PDP: how does the average model prediction change as this feature varies across its range? 2. Overlay 50 randomly sampled ICE curves to show individual variation around the average 3. Highlight the average ICE curve in bold 4. Mark the actual data distribution (rug plot) on the x-axis to show where data is sparse 5. Describe the relationship: monotonic increasing, monotonic decreasing, non-linear, threshold effect? Also create one 2D PDP for the top pair of interacting features (identified from SHAP interaction values). Return all plots and a table summarizing the relationship type for each feature.

Generate a complete SHAP-based model explanation. 1. Compute SHAP values for all predictions in the validation set 2. Global explanations: - Beeswarm plot: feature importance + direction of effect - Bar plot: mean absolute SHAP value per feature (top 20) 3. Dependence plots for the top 3 most important features: - SHAP value on y-axis, feature value on x-axis - Color by the most important interaction feature 4. Local explanations — waterfall plots for: - The most confidently correct prediction - The most confidently wrong prediction - One typical prediction near the decision boundary 5. Plain-English summary: what are the top 3 drivers of high predictions vs low predictions? Return all plots and the plain-English summary.