  Machine learning (ML) algorithms generate a continuous stream of success
stories from various domains and enable many novel applications in
safety-critical systems. With the advent of autonomous driving, ML algorithms
are being used in the automotive domain, where the applicable functional safety
standard is ISO 26262. However, requirements and recommendations provided by
ISO 26262 do not cover specific properties of machine learning algorithms.
Therefore, specific aspects of ML (e.g., dataset requirements, performance
evaluation metrics, lack of interpretability) must be addressed within some
work products, which collect documentation resulting from one or more
associated requirements and recommendations of ISO 26262. In this paper, we
propose how key technical aspects and supporting processes related to
development of ML-based systems can be organized according to ISO 26262 phases,
sub-phases, and work products. We follow the same approach as in the ISO/PAS
21448 standard, which complements ISO 26262, in order to account for edge cases
that can lead to hazards not directly caused by system failure.%, but resulting
from functional insufficiencies of the intended functionality or by reasonably
foreseeable misuse by persons.
