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

Handling Uncertainty in Statistical Performance Claims

Quiz: Handling Uncertainty in Statistical Performance Claims

Safety Cases for Frontier and General Purpose Models

Quiz: Safety Cases for Frontier and General Purpose Models

Inability, Control and Trustworthiness Argument Patterns

Quiz: Inability, Control and Trustworthiness Argument Patterns

Reviewing a Safety Case: Where These Arguments Usually Break

Quiz: Reviewing a Safety Case: Where These Arguments Usually Break

Maintaining the Case as the System and Environment Change

Quiz: Maintaining the Case as the System and Environment Change

Relating a Safety Case to Regulatory Conformity Evidence

Quiz: Relating a Safety Case to Regulatory Conformity Evidence

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

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Quiz: Handling Uncertainty in Statistical Performance Claims

AI Safety Cases: Writing a Structured Argument That a System Is Safe Enough
Handling Uncertainty in Statistical Performance Claims
Quiz: Handling Uncertainty in Statistical Performance Claims

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Question 1 of 5

1. Question

What is a key reason why absolute guarantees cannot be provided in safety claims for AI systems?

AI systems are too expensive to test thoroughly
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The complexity of AI systems and limitations of testing environments create inevitable uncertainty
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Regulatory bodies do not allow safety claims to be quantified
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AI systems are not yet mature enough for reliable safety assessments
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Question 2 of 5

2. Question

According to ISO 13849-1 clause 7.2.1, what must safety functions be designed to handle?

Only predictable system behaviour
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The limitations of clinical trials
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Uncertainties in system behaviour
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The cost of implementing safety measures
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Question 3 of 5

3. Question

Which of the following is a recommended approach for communicating uncertainty in AI performance metrics?

Present single point estimates without any range
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Provide a fixed accuracy rate such as 99.9%
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Give a range such as 'accuracy between 95% and 99% with 90% confidence'
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Avoid mentioning uncertainty to maintain stakeholder confidence
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Question 4 of 5

4. Question

What does ISO 14971 clause 5.2.3 require regarding risk assessment in AI safety claims?

Risk analysis must assume complete data availability
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The limitations of available data must be considered
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Risk levels must be calculated using only historical data
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Risk assessment must exclude any uncertainty components
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Question 5 of 5

5. Question

What is one way to build robust safety arguments for AI systems according to the lesson?

Assume that all edge cases have been tested
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Demonstrate that systems perform adequately even under adverse conditions
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Claim that the system is completely immune to uncertainty
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Avoid documenting any known limitations or constraints
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Correct / 1 Points

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