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AI Safety Cases: Writing a Structured Argument That a System Is Safe Enough

What a Safety Case Is and Where the Practice Comes From

Quiz: What a Safety Case Is and Where the Practice Comes From

Claims, Arguments and Evidence: The Structure of an Assurance Case

Quiz: Claims, Arguments and Evidence: The Structure of an Assurance Case

Goal Structuring Notation and Reading a Safety Case Diagram

Quiz: Goal Structuring Notation and Reading a Safety Case Diagram

Defining the Operating Context and the Safety Requirement

Quiz: Defining the Operating Context and the Safety Requirement

Decomposing a Top Claim Into Testable Sub Claims

Quiz: Decomposing a Top Claim Into Testable Sub Claims

Evidence Types: Testing, Analysis, Process and Field Data

Quiz: Evidence Types: Testing, Analysis, Process and Field Data

Assurance Deficits, Defeaters and Counter Evidence

Quiz: Assurance Deficits, Defeaters and Counter Evidence

Confidence Arguments and Avoiding Circular Reasoning

Quiz: Confidence Arguments and Avoiding Circular Reasoning

Safety Cases for Machine Learning Components

Quiz: Safety Cases for Machine Learning Components

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Quiz: Safety Cases for Machine Learning Components

AI Safety Cases: Writing a Structured Argument That a System Is Safe Enough
Safety Cases for Machine Learning Components
Quiz: Safety Cases for Machine Learning Components

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Question 1 of 5

1. Question

What is a key difference between machine learning systems and traditional software when considering safety cases?

ML systems do not require any safety measures
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ML systems operate with uncertainty and can behave unpredictably
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ML systems are easier to validate than traditional software
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ML systems do not interact with users
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Question 2 of 5

2. Question

Which of the following is NOT identified as a specific risk in machine learning systems?

Data bias
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Model drift
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Unpredictable edge cases
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Lack of user interfaces
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Question 3 of 5

3. Question

What must the data safety component of a machine learning safety case demonstrate?

The system's ability to handle adversarial inputs
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The training data is representative, unbiased, and sufficient
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The model performs well on historical data only
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The system has no monitoring frameworks
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Question 4 of 5

4. Question

Why is it important for a machine learning safety case to include adversarial testing?

To reduce the cost of system development
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To ensure resistance to malicious inputs or manipulation attempts
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To make the system easier to understand
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To increase the speed of data processing
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Question 5 of 5

5. Question

What must be included in the final component of a machine learning safety case regarding system updates?

Only initial testing of the model
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Procedures for testing updates and addressing safety issues through retraining
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A complete redesign of the system architecture
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The exclusion of any further validation after deployment
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