Identify AI Adoption Risks CAIPM Practice Questions (Page 9)

Questions 41–45

1. 41foundation · easy

   What is the primary purpose of identifying AI adoption risks in an organization?

   • ATo eliminate all risks before starting any AI project
   • BTo create a record of risks that can be used to assign blame if a project fails
   • CTo enable informed decision-making and proactive management of potential negative impacts
   • DTo satisfy regulatory requirements that mandate risk identification for all AI projects
   Reveal Solution Discussion Select an answer first
2. 42application · medium

   A regional bank is piloting an AI system to approve small business loans. The model was trained on historical data from the past decade, which included a period of economic recession. The bank's IT team is concerned that the model's performance may degrade when the economy shifts. During the risk assessment, which risk should the team prioritize as the highest impact and likelihood?

   • AData quality risk, because the training data may not represent future economic conditions, leading to inaccurate predictions.
   • BEthical risk, because the model may inadvertently discriminate against certain borrower groups.
   • COperational risk, because the loan approval workflow will be disrupted by the new AI system.
   • DFinancial risk, because the bank may not achieve the expected ROI from the AI system.
   Reveal Solution Discussion Select an answer first
4. 43application · medium

   A healthcare organization is developing an AI system to assist doctors in diagnosing skin conditions from images. The system will use a deep learning model trained on a dataset of dermatological images. The organization is concerned about the model's accuracy for patients with darker skin tones, as the training data is predominantly from lighter skin tones. The program manager needs to mitigate the risk of biased predictions. Which mitigation strategy is most appropriate?

   • ADeploy the model only for patients with lighter skin tones to avoid potential misdiagnosis in other groups.
   • BUse a different AI model that is known to be unbiased, without checking its performance on diverse skin tones.
   • CRely on doctors to manually review all AI predictions to catch any errors, regardless of skin tone.
   • DAugment the training dataset with more images of darker skin tones and validate the model's performance across different skin tone groups.
   Reveal Solution Discussion Select an answer first
5. 44application · medium

   A logistics company is rolling out an AI system that optimizes delivery routes for its drivers. The system will change the daily workflow of drivers, who are used to planning their own routes. The company's operations manager is concerned about resistance from drivers and the potential for decreased productivity during the transition. The program manager needs to mitigate the operational risk of workflow disruption. Which mitigation strategy is most effective?

   • AOffer financial incentives to drivers who adopt the new system quickly, but do not provide training.
   • BAllow drivers to opt out of using the AI system and continue with their own route planning.
   • CInvolve drivers in the design and testing of the AI system and provide comprehensive training before full deployment.
   • DDeploy the AI system immediately to all drivers to minimize the transition period and avoid prolonged disruption.
   Reveal Solution Discussion Select an answer first
6. 45foundation · easy

   Which of the following is a technical risk associated with AI adoption?

   • AInsufficient training data leading to poor model performance
   • BPotential violation of intellectual property rights
   • CLack of employee training on the new system
   • DUnclear accountability for AI decisions
   Reveal Solution Discussion Select an answer first