Answer-first summary for fast verification
Answer: Investigate local feature importance for the specific prediction to understand how each feature influenced the decision for this particular customer., Combine insights from both global feature importance to understand the model's general behavior and local feature importance for the specific prediction to provide a comprehensive explanation.
Local feature importance (C) provides insight into how individual features influenced the specific prediction for the rejected loan application, offering a clear explanation for the decision. Combining this with global feature importance (E) offers a more comprehensive understanding, explaining both the specific decision and the model's general behavior, which is valuable for transparency and regulatory compliance. Reference: [Google Cloud AutoML Tables Documentation on Explainability](https://cloud.google.com/automl-tables/docs/explain).
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As an ML engineer at a bank, you've developed a binary classification model using Google Cloud AutoML Tables to predict whether customers will make loan payments on time, which directly influences loan approval decisions. A customer's loan request was recently rejected, and the bank's risk management department has requested a detailed explanation of the model's decision-making process for this specific case. The bank emphasizes the importance of transparency and accountability in its AI-driven decisions, especially given regulatory compliance requirements. You need to provide a clear, actionable explanation that can be understood by non-technical stakeholders. Which of the following approaches will best meet these requirements? (Choose two options if E is available.)
A
Review the global feature importance percentages on the model evaluation page to explain the general behavior of the model.
B
Use the data summary page to analyze the correlation between features and the target variable, then infer the reasons for the specific rejection.
C
Investigate local feature importance for the specific prediction to understand how each feature influenced the decision for this particular customer.
D
Manually adjust the features of the rejected application to identify which changes would reverse the model's decision, without considering the model's interpretability features.
E
Combine insights from both global feature importance to understand the model's general behavior and local feature importance for the specific prediction to provide a comprehensive explanation.