AAIA · Question #5
While evaluating a complex machine learning (ML) model used for regulatory compliance in a financial institution, which of the following should the IS auditor do to BEST ensure transparency?
The correct answer is D. Use tools that explain model decisions. Transparency in ML means being able to understand and explain how and why the model arrives at specific decisions - not just what the outputs are. Tools such as SHAP (SHapley Additive exPlanations) or LIME provide feature-level explanations of individual model decisions…
Question
While evaluating a complex machine learning (ML) model used for regulatory compliance in a financial institution, which of the following should the IS auditor do to BEST ensure transparency?
Options
- ADocument sources and data processes.
- BCreate dashboards to show outputs.
- CProvide periodic model audit reports.
- DUse tools that explain model decisions.
How the community answered
(48 responses)- A2% (1)
- B6% (3)
- C13% (6)
- D79% (38)
Explanation
Transparency in ML means being able to understand and explain how and why the model arrives at specific decisions - not just what the outputs are. Tools such as SHAP (SHapley Additive exPlanations) or LIME provide feature-level explanations of individual model decisions, directly addressing transparency requirements for regulatory compliance. Documenting data sources (A) addresses data lineage and auditability, not decision-level transparency. Output dashboards (B) show results but not the reasoning behind them. Periodic audit reports (C) provide oversight summaries but do not explain individual model decisions.
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