  As AI systems become prevalent in high stakes domains such as surveillance
and healthcare, researchers now examine how to design and implement them in a
safe manner. However, the potential harms caused by systems to stakeholders in
complex social contexts and how to address these remains unclear. In this
paper, we explain the inherent normative uncertainty in debates about the
safety of AI systems. We then address this as a problem of vagueness by
examining its place in the design, training, and deployment stages of AI system
development. We adopt Ruth Chang's theory of intuitive comparability to
illustrate the dilemmas that manifest at each stage. We then discuss how
stakeholders can navigate these dilemmas by incorporating distinct forms of
dissent into the development pipeline, drawing on Elizabeth Anderson's work on
the epistemic powers of democratic institutions. We outline a framework of
sociotechnical commitments to formal, substantive and discursive challenges
that address normative uncertainty across stakeholders, and propose the
cultivation of related virtues by those responsible for development.
