The question refers to the auditor using technology to uncover anomalies and patterns in decision-making — this is characteristic of data mining.
Data mining is the process of discovering patterns, correlations, and anomalies from large datasets using statistical and computational techniques. It is commonly used during AI audits to detect inconsistencies or risks hidden in the data that may affect ethical or regulatory compliance.
* Predictive analytics is focused on forecasting future outcomes.
* Text analytics deals with extracting insights from unstructured text.
* Sentiment analysis specifically focuses on emotional tone, not decision-making anomalies.
[Reference:, ISO/IEC 42001:2023, Annex A – Data analysis and bias detection, PECB ISO/IEC 42001 Lead Auditor Guide – Section: Use of Data Mining in AI Audits, ISO/IEC TR 24028:2020 – Trustworthiness in AI systems: Role of data mining in audits, , Certainly! Below are the answers to Questions 36 through 38 from Scenario 5, presented in the required format with references to ISO/IEC 42001:2023, ISO/IEC 17021-1:2015, ISO 19011:2018, and PECB’s Lead Auditor Study Guide., , —, ]
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