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.
@article{arxiv.2006.04707,
title = {Principles to Practices for Responsible AI: Closing the Gap},
author = {Daniel Schiff and Bogdana Rakova and Aladdin Ayesh and Anat Fanti and Michael Lennon},
journal= {arXiv preprint arXiv:2006.04707},
year = {2020}
}
Comments
Preprint draft. A version has been submitted to the 2020 European Conference on AI (ECAI) Workshop on "ADVANCING TOWARDS THE SDGs: ARTIFICIAL INTELLIGENCE FOR A FAIR, JUST AND EQUITABLE WORLD (AI4EQ)"