  Many stakeholders struggle to make reliances on ML-driven systems due to the
risk of harm these systems may cause. Concerns of trustworthiness, unintended
social harms, and unacceptable social and ethical violations undermine the
promise of ML advancements. Moreover, such risks in complex ML-driven systems
present a special challenge as they are often difficult to foresee, arising
over periods of time, across populations, and at scale. These risks often arise
not from poor ML development decisions or low performance directly but rather
emerge through the interactions amongst ML development choices, the context of
model use, environmental factors, and the effects of a model on its target.
Systems safety engineering is an established discipline with a proven track
record of identifying and managing risks even in high-complexity sociotechnical
systems. In this work, we apply a state-of-the-art systems safety approach to
concrete applications of ML with notable social and ethical risks to
demonstrate a systematic means for meeting the assurance requirements needed to
argue for safe and trustworthy ML in sociotechnical systems.
