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AMLAS outline

Example 1 - Pedestrian identification
Note 1 - Consider architectural features
Note 2 - Consider human contribution
System safety requirements [A]
Description of the system environment [B]
System description [C]
ML component description [D]

ML safety assurance scoping argument pattern [F]

Example 5 - Defining acceptable risk level
Example 6 - Translating safety requirements
Example 7 - Performance and robustness
Note 5 - Semantic gap
Note 6 - Under‐specificity
Note 7 - Safety criticality
Note 8 - Performance and robustness
Note 9 - Measurable features
Note 10 - Overall performance
Note 11 - Dimensions of variation
Note 12 - Tolerable risk

ML safety requirements argument pattern [I]

Data requirements [L]
Data requirements justification report [M]

Example 14 - Vehicle gathering video data
Example 15 - Handling missing data
Example 16 - Retinal scans
Example 17 - Augmenting the dataset
Example 18 - ML classifier for cancer
Note 16 - Justifying collected data
Note 17 - Rationale for existing data sources
Note 18 - Using simulators
Note 19 - Preprocessing of data
Note 20 - Labelling data
Note 21 - Combinations of variation
Data generation log [Q]

Example 19 - JAAD open dataset
Example 20 - Using US data in the UK
Example 21 - Data collection using controlled trials
Example 22 - Completeness of datasets
Example 23 - Occlusion
Note 22 - Financial and practical concerns
Note 23 - Simulation for data augmentation
Note 24 - Context-specific features
Note 25 - Dimensions of variability
Note 26 - Quantisation
Note 27 - Balance of datasets
Note 28 - Imbalanced dataset

ML data argument pattern [R]

Example 24 - Medical prognosis problems
Example 25 - Deep Neural Networks
Example 26 - Data samples from a photo simulator
Note 29 - Overfitting to training data
Note 30 - Trade-offs
Model development log [U]

Example 30 - Image analysis
Example 31 - Perception pipeline
Internal test results [X]

Model learning argument pattern [W]

Example 33 - Vehicle stopping distance
Example 34 - Contextual mutators
Example 35 - DeepMind retinal diagnosis system
Example 36 - Contextually meaningful values
Example 37 - Airborne collision avoidance system
Note 31 - Development and verification independence
Note 32 - Independence from verification data
Note 33 - Test completeness
Note 34 - Human evaluation
Note 35 - Point-wise robustness
ML verification evidence [Z]
Verification log [AA]

ML verification argument pattern [BB]

Test the integration

Once the ML model has been integrated into the wider system, the integration needs to be tested to check that the system safety requirements ([A]) are satisfied. This activity requires a defined set of operational scenarios ([EE]) against which the behaviour of the system, as implemented in ML, can be tested.

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