Toward Operationalizing Pluralistic Normative Principles for AI Systems

Abstract

The ethical issues surrounding the development and deployment of AI are becoming very critical as these technologies evolve and affect more facets of society. To make AI systems more beneficial and reduce their potential negative impact on public life, international communities, as well as local and global organizations, are working to elicit normative principles that prescribe what AI systems should and should not do in order to remain trustworthy. However, these principles are new, ambiguous, and very high-level, and there are still no clear standards guiding developers on how to operationalize them in practice. I argue that, operationalizing such principles requires collaboration with multidisciplinary experts to ground them in the context of specific AI solutions and their deployment environments. Otherwise, the problem remains open-ended and difficult to apply in practice. In my thesis, I aim to address this gap by building a bridge between multidisciplinary normative experts and AI developers to operationalize high-level normative principles for AI systems with open capabilities but specific tasks and deployment contexts. I present here my early results, current progress, and future research directions.