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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

1 of 2

Previous Lesson

Quiz: Protected Characteristics and Proxy Variables

AI Bias Testing: Measuring Fairness With Numbers You Can Defend
Protected Characteristics and Proxy Variables
Quiz: Protected Characteristics and Proxy Variables

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Question 1 of 5

1. Question

Which of the following is NOT listed as a protected characteristic under UK law?

Age
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Sexual orientation
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Political opinion
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Disability
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Question 2 of 5

2. Question

What is one example of a proxy variable that might indirectly reflect protected characteristics?

A candidate's favourite colour
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The name of their university
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The time they submitted their application
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The type of computer they used
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Question 3 of 5

3. Question

According to the lesson, what must practitioners do early in their AI bias testing framework?

Ensure all data is anonymised
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Identify which protected characteristics might be relevant
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Compare results with previous models
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Train staff on data privacy laws
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Question 4 of 5

4. Question

Why is it important to examine outcomes across protected characteristic groups rather than treating them as single categories?

It makes data easier to collect
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It allows for intersectional approaches that reveal different experiences
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It reduces the cost of testing
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It avoids the need for statistical analysis
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Question 5 of 5

5. Question

What should organisations do when proxy variables are identified in their AI systems?

Remove them from all datasets immediately
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Determine whether they represent legitimate business needs or potential bias
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Replace them with more complex algorithms
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Ignore them unless they cause obvious errors
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