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Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept

What Changes for Data Science When AI Becomes Regulated

Quiz: What Changes for Data Science When AI Becomes Regulated

Problem Framing: Stating Intended Purpose and Out of Scope Uses

Quiz: Problem Framing: Stating Intended Purpose and Out of Scope Uses

Dataset Documentation, Provenance and Licence Checks

Quiz: Dataset Documentation, Provenance and Licence Checks

Representativeness, Coverage Gaps and Known Data Errors

Quiz: Representativeness, Coverage Gaps and Known Data Errors

Personal Data, Special Category Data and Lawful Basis

Quiz: Personal Data, Special Category Data and Lawful Basis

Splitting Data Honestly and Avoiding Leakage

Quiz: Splitting Data Honestly and Avoiding Leakage

Choosing Metrics That Match the Decision Being Automated

Quiz: Choosing Metrics That Match the Decision Being Automated

Fairness Metrics and Why They Cannot All Be Satisfied at Once

Quiz: Fairness Metrics and Why They Cannot All Be Satisfied at Once

Subgroup Analysis and Reporting Performance Where It Is Weakest

Quiz: Subgroup Analysis and Reporting Performance Where It Is Weakest

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Quiz: Representativeness, Coverage Gaps and Known Data Errors

Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept
Representativeness, Coverage Gaps and Known Data Errors
Quiz: Representativeness, Coverage Gaps and Known Data Errors

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Question 1 of 5

1. Question

What is representativeness in a training dataset?

The dataset has no missing values
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The dataset reflects the distribution of the population the model will serve
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The dataset has equal numbers of all classes
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The dataset is stored in a standard format
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Question 2 of 5

2. Question

What is a coverage gap?

A field with missing values
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A population missing from the training dataset
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A difference between train and test accuracy
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A period when data was not collected
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Question 3 of 5

3. Question

How should you respond to a coverage gap of 5 percent ethnic minority applicants when the target population is 20 percent?

Ignore it and deploy the model
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Accept the gap, document it and state that the model is not suitable for that underrepresented group
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Adjust model predictions to correct for the gap
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Use synthetic data to fill the gap
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Question 4 of 5

4. Question

What is a known data error that would be serious in a credit model?

1 percent missing values in age
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1 percent mislabelling of the target variable (default or not)
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2 percent duplicate records
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3 percent outliers in income
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Question 5 of 5

5. Question

Why should you record data quality issues rather than try to hide them?

Because regulators require it
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Because it demonstrates you have audited your data and understand limitations
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Because it makes the model more accurate
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Because data quality issues are illegal
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