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AI Training for Public Sector Staff: Lawful, Transparent and Challengeable Decisions

Explaining a Decision to a Citizen in Plain Language

Quiz: Explaining a Decision to a Citizen in Plain Language

Procurement and Supplier Transparency for Public AI

Quiz: Procurement and Supplier Transparency for Public AI

Working With Legacy Data and Historic Bias in Records

Quiz: Working With Legacy Data and Historic Bias in Records

Pilots, Evaluation and Deciding Not to Scale

Quiz: Pilots, Evaluation and Deciding Not to Scale

Staff Guidance and Approved Tools for Generative AI Use

Quiz: Staff Guidance and Approved Tools for Generative AI Use

Reporting, Oversight and Answering Scrutiny Committees

Quiz: Reporting, Oversight and Answering Scrutiny Committees

Final exam: AI Training for Public Sector Staff: Lawful, Transparent and Challengeable Decisions

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Quiz: Working With Legacy Data and Historic Bias in Records

AI Training for Public Sector Staff: Lawful, Transparent and Challengeable Decisions
Working With Legacy Data and Historic Bias in Records
Quiz: Working With Legacy Data and Historic Bias in Records

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Question 1 of 5

1. Question

Why is historical data often biased?

Data becomes biased over time
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Past decisions may have been discriminatory or reflected systemic unfairness that the organisation is now trying to overcome
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Historical data is always perfect because it records actual events
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Bias in data is impossible
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Question 2 of 5

2. Question

What is the risk when an algorithm is trained on historically biased data?

The algorithm will definitely be more fair than humans
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The algorithm will learn to reproduce the historical bias
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The data will become newer as time passes
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Historical data cannot be used in algorithms
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Question 3 of 5

3. Question

How can an organisation assess what bias a dataset contains?

Assume the data is unbiased
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Examine the data's origins and test the algorithm's outcomes for different protected groups
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Ask the vendor whether the data is biased
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Use the data without testing because historical data is official
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Question 4 of 5

4. Question

What is one way to address bias detected in algorithm training data?

Ignore the bias
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Remove variables that carry bias or that are proxies for protected characteristics
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Use only data from people who agree with the algorithm
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Train the algorithm to discriminate in the opposite direction
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Question 5 of 5

5. Question

What should an algorithmic transparency record disclose about bias in training data?

Nothing, because training data is confidential
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That the system was trained on historical data, what testing has been done for bias, and what safeguards are in place
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Only positive results
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That the algorithm is completely unbiased
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