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AI Bias Testing: Measuring Fairness With Numbers You Can Defend

What Bias Means Statistically, Legally and Socially

Quiz: What Bias Means Statistically, Legally and Socially

Where Bias Enters: Data, Labels, Features and Deployment

Quiz: Where Bias Enters: Data, Labels, Features and Deployment

Protected Characteristics and Proxy Variables

Quiz: Protected Characteristics and Proxy Variables

Collecting Demographic Data Lawfully for Testing

Quiz: Collecting Demographic Data Lawfully for Testing

Group Fairness Metrics: Parity, Equal Opportunity and Equalised Odds

Quiz: Group Fairness Metrics: Parity, Equal Opportunity and Equalised Odds

Impact Ratios, the Four Fifths Rule and Legal Thresholds

Quiz: Impact Ratios, the Four Fifths Rule and Legal Thresholds

Individual and Counterfactual Fairness Approaches

Quiz: Individual and Counterfactual Fairness Approaches

Why Fairness Definitions Conflict Mathematically

Quiz: Why Fairness Definitions Conflict Mathematically

Sample Size, Confidence and Testing Small Groups Honestly

Quiz: Sample Size, Confidence and Testing Small Groups Honestly

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Quiz: Why Fairness Definitions Conflict Mathematically

AI Bias Testing: Measuring Fairness With Numbers You Can Defend
Why Fairness Definitions Conflict Mathematically
Quiz: Why Fairness Definitions Conflict Mathematically

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Question 1 of 5

1. Question

According to the lesson, what is a fundamental tension in fairness definitions within AI systems?

They are too complex to implement in practice
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Different definitions make different assumptions about equitable treatment
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They are not aligned with existing legal frameworks
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They require too much computational power to test
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Question 2 of 5

2. Question

What does demographic parity require regarding positive outcomes across demographic groups?

The probability of a positive outcome must vary by group
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The probability of a positive outcome must match overall population rates
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The probability of a positive outcome must remain constant across groups
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The probability of a positive outcome must be based on individual merit
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Question 3 of 5

3. Question

Which clause of ISO 20083-1 is mentioned as addressing fairness definitions in the lesson?

Clause 5.2.1
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Clause 5.3.2
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Clause 5.4.1
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Clause 5.5.3
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Question 4 of 5

4. Question

In the example of financial lending, what is a consequence of satisfying demographic parity?

It ensures equal approval rates across gender groups
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It always leads to higher credit scores for women
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It makes the model more effective at predicting risk
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It removes any existing bias from the training data
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Question 5 of 5

5. Question

What is one practical approach suggested for managing conflicts between fairness definitions?

Avoid using any fairness definitions in model development
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Measure the mathematical distance between different definitions
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Choose only one definition and ignore others
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Ensure that all definitions are satisfied simultaneously
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