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Question 55 - Professional Machine Learning Engineer discussion

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You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

A.
Redaction, reproducibility, and explainability
Answers
A.
Redaction, reproducibility, and explainability
B.
Traceability, reproducibility, and explainability
Answers
B.
Traceability, reproducibility, and explainability
C.
Federated learning, reproducibility, and explainability
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C.
Federated learning, reproducibility, and explainability
D.
Differential privacy federated learning, and explainability
Answers
D.
Differential privacy federated learning, and explainability
Suggested answer: B

Explanation:

Before building an insurance approval model, an ML engineer should consider the factors of traceability, reproducibility, and explainability, as these are important aspects of responsible AI and fairness in a regulated domain. Traceability is the ability to track the provenance and lineage of the data, models, and decisions throughout the ML lifecycle. It helps to ensure the quality, reliability, and accountability of the ML system, and to comply with the regulatory and ethical standards. Reproducibility is the ability to recreate the same results and outcomes using the same data, models, and parameters. It helps to verify the validity, consistency, and robustness of the ML system, and to debug and improve the performance. Explainability is the ability to understand and interpret the logic, behavior, and outcomes of the ML system. It helps to increase the transparency, trust, and confidence of the ML system, and to identify and mitigate any potential biases, errors, or risks. The other options are not as relevant or comprehensive as this option. Redaction is the process of removing sensitive or confidential information from the data or documents, but it is not a factor that the ML engineer should consider before building the model, as it is more related to the data preparation and protection. Federated learning is a technique that allows training ML models on decentralized data without transferring the data to a central server, but it is not a factor that the ML engineer should consider before building the model, as it is more related to the model architecture and privacy preservation. Differential privacy is a method that adds noise to the data or the model outputs to protect the individual privacy of the data subjects, but it is not a factor that the ML engineer should consider before building the model, as it is more related to the model evaluation and deployment.Reference:

Responsible AI documentation

Traceability documentation

Reproducibility documentation

Explainability documentation

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asked 18/09/2024
Niall Dempsey
35 questions
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