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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: Choosing Metrics That Match the Decision Being Automated

Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept
Choosing Metrics That Match the Decision Being Automated
Quiz: Choosing Metrics That Match the Decision Being Automated

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Question 1 of 5

1. Question

Why is accuracy sometimes a misleading metric?

It requires too much computation
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In imbalanced data, accuracy can be high by predicting the majority class always
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Accuracy only works for binary classification
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Accuracy requires a validation set
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Question 2 of 5

2. Question

In fraud detection, which error is usually more costly: missing a fraud or incorrectly flagging a transaction as fraud?

They cost the same
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Missing a fraud is more costly
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Incorrectly flagging is more costly
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It depends on the bank
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Question 3 of 5

3. Question

What is recall?

The accuracy of the model
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Of all true positives, how many did the model identify
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The percentage of positive predictions that are correct
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The speed of the model
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Question 4 of 5

4. Question

If a loan approval model has high precision but low recall, what does that mean?

Most approved loans default
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Of loans approved, most repay, but many creditworthy applicants are declined
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The model is inaccurate
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The model is unbiased
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Question 5 of 5

5. Question

Should you document your choice of metric?

No, standard metrics are always appropriate
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Yes, explain why this metric matches the decision and what errors cost
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Only if regulators ask
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Only for complex models
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