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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]

Define data requirements

Data plays a particularly important role in machine learning with data encoding the requirements that will be embodied in the resulting ML model. ML data requirements shall therefore be defined to ensure it is possible to develop a machine learnt model that satisfies the ML safety requirements. This activity requires as input the ML safety requirements ([H]) as described in Stage 2 and, from these requirements, data requirements ([L]) shall be generated. Of particular interest in the development of data requirements are those safety requirements which pertain to the description of the system environment.

Artefacts

Continue to: Activity 7. Generate ML data

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