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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: Personal Data, Special Category Data and Lawful Basis

Safe AI for Data Scientists: Evidence, Documentation and Testing Regulators Accept
Personal Data, Special Category Data and Lawful Basis
Quiz: Personal Data, Special Category Data and Lawful Basis

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Question 1 of 5

1. Question

What is special category data under GDPR?

Any data stored in a database
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Data revealing race, health, religion or trade union membership
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Data that is more than 5 years old
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Data used to train machine learning models
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Question 2 of 5

2. Question

What is the strongest lawful basis for training a model on customer data?

Legitimate interests
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Explicit informed consent
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Legal obligation
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Public task
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Question 3 of 5

3. Question

Can you train a model on health data without consent?

Yes, if it improves accuracy
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Yes, if stored securely
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Rarely, special category data requires very strong justification
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Yes, if aggregated
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Question 4 of 5

4. Question

What should you do if you want to train a model on customer data collected for a different purpose?

Use it, since you already have it
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Evaluate whether model training is compatible with the original purpose
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Get new consent only if challenged
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Document that the original purpose was compatible
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Question 5 of 5

5. Question

Should you include race as a predictor in a credit scoring model?

Yes, if it improves accuracy
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Only if the model is audited
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No, not unless you have very strong justification and explicit consent
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Only in training, not in predictions
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