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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]

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]

Instantiate ML data argument pattern

This activity requires as input the ML data argument pattern ([R]), as well as the artefacts from the previous activities of this stage ([L], [M], [N], [O], [P], [Q] and [S]). The activity uses the activities and outputs from the previous stages to create an instantiated ML data argument ([T]).

Artefacts

[R] - ML data argument pattern

Continue to: Stage 4. Model learning

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