  Companies have considered adoption of various high-level artificial
intelligence (AI) principles for responsible AI, but there is less clarity on
how to implement these principles as organizational practices. This paper
reviews the principles-to-practices gap. We outline five explanations for this
gap ranging from a disciplinary divide to an overabundance of tools. In turn,
we argue that an impact assessment framework which is broad, operationalizable,
flexible, iterative, guided, and participatory is a promising approach to close
the principles-to-practices gap. Finally, to help practitioners with applying
these recommendations, we review a case study of AI's use in forest ecosystem
restoration, demonstrating how an impact assessment framework can translate
into effective and responsible AI practices.
