AIF-C01 Question 185
Select 3A financial services company is using a machine learning model to predict creditworthiness for loan applications. Some customers have been denied loans and are requesting explanations for the decisions. Why is it important to use a transparent and explainable model in this scenario?
- A
It helps build customer trust by providing clear reasons for the decisions made by the model.
- B
It ensures compliance with regulatory requirements for fairness and accountability.
- C
It improves the predictive accuracy of the model for future loan applications.
- D
It allows stakeholders to detect and mitigate potential biases in the model.
- E
It reduces the need for human oversight in the decision-making process.
Show answer and explanation
Correct answers: A, B, D
Explanation
In scenarios involving high-stakes decisions, such as loan approvals, transparent and explainable models are crucial for building trust, ensuring regulatory compliance, and identifying potential biases. While explainability does not directly improve model accuracy or eliminate the need for human oversight, it is invaluable in increasing the accountability and fairness of AI-driven decisions.
- A. Correct.
Providing clear reasons for decisions builds trust with customers who may otherwise feel alienated or mistreated by opaque decision-making processes.
- B. Correct.
Many industries, including financial services, are subject to regulations that require transparency, fairness, and accountability in automated decision-making systems.
- C. Incorrect.
While explainability is important for understanding model decisions, it does not directly improve the predictive accuracy of the model.
- D. Correct.
Transparency allows for the identification of biases or unfair patterns in the model, enabling stakeholders to address these issues proactively.
- E. Incorrect.
Explainability does not eliminate the need for human oversight; in fact, it often enhances human understanding and decision-making.