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 shou

Question

Options

• ARedaction, reproducibility, and explainability
• BTraceability, reproducibility, and explainability
• CFederated learning, reproducibility, and explainability
• DDifferential privacy, federated learning, and explainability

• A
  16% (4)
• B
  72% (18)
• C
  4% (1)
• D
  8% (2)

Explanation

When developing an insurance approval model, it's crucial to consider several factors to ensure that the model is fair, accurate, and compliant with regulations. The factors to consider include: Traceability: It's important to be able to trace the data used to build the model and the decisions made by the model. This is important for transparency and accountability. Reproducibility: The model should be built in a way that allows for its reproducibility. This means that other researchers should be able to reproduce the same results using the same data and Explainability: The model should be able to provide clear and understandable explanations for its decisions. This is important for building trust with customers and ensuring compliance with Other factors that may also be important to consider, depending on the specific context of the insurance company and its customers, include data privacy and security, fairness, and bias

No community discussion yet for this question.