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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: Splitting Data Honestly and Avoiding Leakage

Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept
Splitting Data Honestly and Avoiding Leakage
Quiz: Splitting Data Honestly and Avoiding Leakage

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Question 1 of 5

1. Question

What is the gold standard approach to splitting data?

Random split 50/50
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Temporal split: train on past, test on future
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Stratified split to match class distributions
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No split: train and test on the same data
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Question 2 of 5

2. Question

What is data leakage?

Storing data in an unencrypted location
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Information from the test set influencing the model during training
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Using too many variables in the model
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Splitting data unevenly
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Question 3 of 5

3. Question

If you want to standardise variables, when should you calculate the mean and standard deviation?

From the full dataset before splitting
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From the training set only
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From the test set only
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Separately for each cross validation fold
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Question 4 of 5

4. Question

In a fraud detection model, which variable would represent data leakage?

Customer account age
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Account flagged as fraudulent by another system
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Previous transaction amounts
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Time of day
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Question 5 of 5

5. Question

Can you use the test set to decide between model architectures?

Yes, if you validate on the validation set
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Yes, if you report test performance honestly
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No, using test set for any decision during development introduces bias
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Yes, if the test set is large enough
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