From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML. (arXiv:2210.03535v1 [cs.HC])

Inappropriate design and deployment of machine learning (ML) systems leads to
negative downstream social and ethical impact -- described here as social and
ethical risks -- for users, society and the environment. Despite the growing
need to regulate ML systems, current processes for assessing and mitigating
risks are disjointed and inconsistent. We interviewed 30 industry practitioners
on their current social and ethical risk management practices, and collected
their first reactions on adapting safety engineering frameworks into their
practice -- namely, …

arxiv engineering frameworks responsible ml safety safety engineering