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Answer: Traceability, reproducibility, and explainability
The correct answer is B: Traceability, reproducibility, and explainability. These factors are crucial for building a responsible and compliant insurance approval model in a regulated environment. Traceability ensures that all data and decisions can be audited and traced back to their sources, which is essential for compliance and accountability. Reproducibility ensures that the model's results can be consistently reproduced, which is important for validating the model's reliability. Explainability ensures that the model's decisions can be understood and communicated clearly to stakeholders, including customers and regulators, which is often a legal requirement in many jurisdictions.
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You are an ML engineer at a regulated insurance company, which means that you must comply with various industry regulations and standards. You are tasked with developing an insurance approval model that decides to either accept or reject insurance applications from potential customers. Considering the regulated environment, what factors are most critical to address before building the model?
A
Redaction, reproducibility, and explainability
B
Traceability, reproducibility, and explainability
C
Federated learning, reproducibility, and explainability
D
Differential privacy, federated learning, and explainability