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AI Sampling Methods: Statistical vs Judgmental Approaches
How do auditors approach the massive datasets used to build and operate artificial intelligence systems? This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series explores AI data sampling — covering the differences between traditional and AI auditing, what to do when full-population testing is out of reach, and the specific sampling methods auditors can use to draw meaningful, defensible conclusions about a system’s data.
What this episode covers
- Why auditing AI feels different — vast, diverse datasets make planning the sampling method during design and scoping essential.
- Data population sample testing — when full-population testing is feasible and when to shift focus elsewhere.
- Validity confidence — evaluating the processes and systems used to collect and organize data when full testing is impossible.
- Random sampling — selecting records blindly from the total dataset using simple selection mechanisms.
- Stratified sampling — grouping data into distinct layers and pulling representative records from each.
- Data splitting considerations — mirroring the developers’ train/validate/test ratio in the audit sample.
- Judgmental (nonprobability) sampling — hand-picking high-risk records using deep subject matter expertise.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
Why is AI data sampling different from traditional auditing?
In a standard financial audit the data is usually contained and manageable, but AI relies on an incredibly large, diverse volume of data pulled from many external and internal sources. Because the datasets are so enormous it is impossible to test the entire population, like trying to inspect every drop of water in a reservoir. The auditor must carefully plan the sampling method and sample size during the initial design and scoping phases, rather than manually redoing the model’s complex math.
What happens when the AI data is too large to test fully?
When the data is too large to test, you shift focus from the data itself to evaluating the processes and systems used to collect and organize it, like auditing a giant bakery’s recipe, suppliers, and oven temperatures instead of tasting every cookie. This is called measuring validity confidence, checking whether the type and variety of data are trustworthy so you can spot integration risks and hidden vulnerabilities.
What are the three main AI data sampling methods?
The three main methods are random sampling, where you select records blindly from the total dataset; stratified sampling, where you group the data into distinct layers such as income brackets and pick equally from each; and data splitting considerations, where if developers split data into train, validate, and test buckets at a ratio like sixty, twenty, and twenty, your audit sample must follow that exact same ratio.
What is judgmental sampling in AI auditing?
Judgmental sampling is a type of nonprobability sampling, meaning the selection is not random or based on mathematical chance. Instead the sample is hand-picked based purely on the auditor’s professional intuition and experience, demanding deep subject matter expertise. For an AI predicting bridge failures, you would use civil engineering knowledge to deliberately sample data from bridges in extreme weather or unique designs where the risks hide.
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Reference: This article is based on concepts discussed in AI Sampling Methods: Statistical vs Judgmental Approaches.