Principles to Practices for Responsible AI: Closing the Gap

• URL: http://arxiv.org/abs/2006.04707v1
• Date: Mon, 8 Jun 2020 16:04:44 GMT
• Title: Principles to Practices for Responsible AI: Closing the Gap
• Authors: Daniel Schiff and Bogdana Rakova and Aladdin Ayesh and Anat Fanti and Michael Lennon
• Abstract summary: We argue that an impact assessment framework is a promising approach to close the principles-to-practices gap. 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.
• Score: 0.1749935196721634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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