試験AAIA-JPN トピック1 問題66 スレッド

Clustering, especially in sensitive domains like healthcare, can inadvertently expose confidential patient data if the resulting groups are too specific or reveal underlying health conditions. The AAIA™ Study Guide warns that clustering can increase privacy risks when small, homogenous groups are formed that effectively re-identify individuals or reveal sensitive traits.
"Clustering results must be carefully reviewed to prevent indirect re-identification or unintended exposure of sensitive traits. Ethical handling of aggregated patient data is essential to protect individual privacy." While A and B involve general concerns, and C focuses on performance, D directly addresses the most significant privacy threat: exposure through cluster outputs.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Anonymization and Re-identification Risks"