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?
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?
When developing an insurance approval model in a regulated industry, it is crucial to include traceability, reproducibility, and explainability. Traceability ensures that one can follow the data used in the model and understand the decisions made, which is important for transparency and accountability. Reproducibility means that the model can be tested and verified independently by duplicate results using the same data and methods, ensuring its reliability. Explainability is essential to provide understandable explanations for the model's decisions, critical for building trust and ensuring compliance with regulations.
I think the answer should be B. as I review the OECD document on impact of AI on insurance, the document mention explainability, traceable. However, open for discussion. https://www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.pdf
Should be B
I think it should be A, as it is regulated, so need to have PII
B. Traceability, reproducibility, and explainability 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 methods. 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 regulations. 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 mitigation.
B. Traceability, reproducibility, and explainability are the most important factors to consider before building an insurance approval model. Traceability ensures that the data used in the model is reliable and can be traced back to its source. Reproducibility ensures that the model can be replicated and tested to ensure its accuracy and fairness. Explainability ensures that the model's decisions can be explained to customers and regulators in a transparent manner. These factors are crucial for building a trustworthy and compliant model for an insurance company. Redaction is also important for protecting sensitive customer information, but it is not as critical as the other factors listed. Federated learning and differential privacy are techniques used to protect data privacy, but they are not necessarily required for building an insurance approval model.
B. Traceability, reproducibility, and explainability. Traceability: This involves maintaining records of the data, decisions, and processes used in the model. This is crucial in regulated industries for audit purposes and to ensure compliance with regulatory standards. It helps in understanding how the model was developed and how it makes decisions. Reproducibility: Ensuring that the results of the model can be reproduced using the same data and methods is vital for validating the model's reliability and for future development or debugging. Explainability: Given the significant impact of the model’s decisions on individuals' lives, it's crucial that the model's decisions can be explained in understandable terms. This is not just a best practice in AI ethics; in many jurisdictions, it's a legal requirement under regulations that mandate transparency in automated decision-making.
you are a lifesaver, sum sum. thank you
Traceability is a key factor due to GDPR laws
should be D as all of the techniques abide to any problems related to insurance
Checking Google documents, it seems D.
Please mention the links
went with B
B should be True
Correct answer is "B"
ans: B
B. Traceability, reproducibility, and explainability 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 methods. 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 regulations. 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 mitigation.
Went with B
A kinda makes sense here, because redaction means remove sensitive or private information before sharing it..
B) Traceability, Reproducibility and Explainability
Agree with B