AAISM · Question #220
An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data. Which of the following is the MOST effective data cleaning technique to improve model perfor
The correct answer is B. Applying statistical methods to address missing data and reduce bias. The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction. Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related
Question
An organization using an AI model for financial forecasting identifies inaccuracies caused by missing data. Which of the following is the MOST effective data cleaning technique to improve model performance?
Options
- AIncreasing the frequency of model retraining with the existing data set
- BApplying statistical methods to address missing data and reduce bias
- CDeleting outlier data points to prevent unusual values impacting the model
- DTuning model hyperparameters to increase performance and accuracy
How the community answered
(30 responses)- B93% (28)
- C3% (1)
- D3% (1)
Explanation
The AAISM study content emphasizes that data quality management is a central pillar of AI risk reduction. Missing data introduces bias and undermines predictive accuracy if not addressed systematically. The most effective remediation is to apply statistical imputation and related methods to fill in or adjust for missing values in a way that minimizes bias and preserves data integrity. Retraining on flawed data does not solve the underlying issue. Deleting outliers may harm model robustness, and hyperparameter tuning optimizes model mechanics but cannot resolve missing information. Therefore, the proper corrective technique for missing data is the application of statistical methods to reduce bias.
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