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As AI systems become prevalent in high stakes domains such as surveillanceand healthcare, researchers now examine how to design and implement them in asafe manner. However, the potential harms caused by systems to stakeholders incomplex social contexts and how to address these remains unclear. In thispaper, we explain the inherent normative uncertainty in debates about thesafety of AI systems. We then address this as a problem of vagueness byexamining its place in the design, training, and deployment stages of AI systemdevelopment. We adopt Ruth Chang's theory of intuitive comparability toillustrate the dilemmas that manifest at each stage. We then discuss howstakeholders can navigate these dilemmas by incorporating distinct forms ofdissent into the development pipeline, drawing on Elizabeth Anderson's work onthe epistemic powers of democratic institutions. We outline a framework ofsociotechnical commitments to formal, substantive and discursive challengesthat address normative uncertainty across stakeholders, and propose thecultivation of related virtues by those responsible for development.

Hard Choices in Artificial Intelligence: Addressing Normative Uncertainty through Sociotechnical Commitments