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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: Confidence Arguments and Avoiding Circular Reasoning

AI Safety Cases: Writing a Structured Argument That a System Is Safe Enough
Confidence Arguments and Avoiding Circular Reasoning
Quiz: Confidence Arguments and Avoiding Circular Reasoning

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Question 1 of 5

1. Question

What is the primary method for building confidence in AI safety arguments according to the lesson?

Relying on expert opinions alone
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Using assumptions about system performance
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Examining evidence carefully rather than assuming safety
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Comparing the system to similar AI models
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Question 2 of 5

2. Question

Which type of evidence is NOT mentioned as supporting safety claims in the lesson?

Performance metrics from controlled testing
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Comparative analysis against existing systems
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Failure rate data from similar systems
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User feedback from social media
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Question 3 of 5

3. Question

What is an example of circular reasoning in AI safety arguments?

Claiming a system is safe because it meets safety requirements that are defined by the system's safety properties
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Using data from real-world usage to prove safety
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Comparing the system to other AI models with similar accuracy
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Showing that the system avoids failure through testing
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Question 4 of 5

4. Question

Why is it important to define safety through measurable outcomes rather than system characteristics?

Measurable outcomes are easier to collect than system characteristics
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Measurable outcomes prevent circular reasoning in safety arguments
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System characteristics are not relevant to safety claims
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Measurable outcomes are always more accurate than system characteristics
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Question 5 of 5

5. Question

What must practitioners ensure when constructing safety arguments to maintain logical integrity?

The evidence should be gathered from multiple sources
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Each step in the argument must directly support the next step
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The argument must include at least three different types of evidence
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The conclusion must be stated before the evidence is presented
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