Identify AI Adoption Risks CAIPM Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   Which of the following is an operational risk in AI adoption?

   • AEmployees lack the skills to maintain the AI system
   • BThe AI model has a high error rate on new data
   • CThe AI system inadvertently discriminates against a protected group
   • DThe AI vendor's patent is challenged in court
   Reveal Solution Discussion Select an answer first
2. 22application · medium

   An insurance company is developing an AI system to automatically assess claims and determine payouts. The system will use customer data, including medical records and financial information. The company operates in a jurisdiction with strict data privacy laws. The program manager has identified a risk that the AI system may inadvertently expose sensitive customer data through a security vulnerability. What is the most appropriate mitigation strategy?

   • AObtain consent from all customers to use their data for the AI system, which will eliminate any privacy concerns.
   • BConduct a thorough security assessment and implement encryption and access controls to protect customer data.
   • CDelay the project until the legal department can provide a definitive interpretation of the privacy laws.
   • DAnonymize all customer data before feeding it to the AI system, so that even if exposed, the data cannot be traced to individuals.
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   An organization introduces an AI tool that automates a manual process. Employees are uncertain about their new roles and resist the change. Which type of risk is most directly illustrated?

   • AFinancial risk
   • BTechnical risk
   • CEthical risk
   • DOperational risk
   Reveal Solution Discussion Select an answer first
5. 24expert · hard

   A financial institution is developing an AI system for credit scoring. The system will use a large dataset of customer financial history. The program manager has identified several risks: (1) the model may be biased against certain ethnic groups, (2) the model's decisions may not be explainable to regulators, (3) the training data may contain outdated information that does not reflect current economic conditions, and (4) the system integration with the core banking system may be complex. The institution has a legal obligation to ensure fair lending practices and must comply with regulatory requirements for model explainability. The program manager needs to choose a mitigation strategy that addresses the most critical risks while balancing cost and effort. Which strategy is most appropriate?

   • AOutsource the model development to a third-party vendor that specializes in regulatory compliance.
   • BFocus only on bias mitigation by re-sampling the training data to ensure equal representation of all ethnic groups.
   • CUse a simple, interpretable model (e.g., logistic regression) instead of a complex deep learning model to ensure explainability.
   • DImplement a comprehensive model governance framework that includes bias testing, explainability tools, and regular audits.
   Reveal Solution Discussion Select an answer first
6. 25application · medium

   A financial institution is deploying an AI system for fraud detection. The system will process sensitive customer data and must comply with industry regulations. The project team has identified risks including data privacy, model accuracy, and system integration. Which mitigation strategy would best address the legal and compliance risk?

   • AIntegrate the system with existing infrastructure to ensure smooth operation.
   • BImplement data encryption and access controls, and conduct regular compliance audits.
   • CImprove the model's accuracy by using more training data.
   • DTrain staff on how to use the new system effectively.
   Reveal Solution Discussion Select an answer first