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Principles to Practices for Responsible AI: Closing the Gap

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arxiv 2006.04707 v1 pith:OBIAY2CW submitted 2020-06-08 cs.CY cs.AI

Daniel Schiff , Bogdana Rakova , Aladdin Ayesh , Anat Fanti , Michael Lennon This is my paper · ORCID
classification cs.CYcs.AI
keywords practicesprinciplesresponsibleassessmentframeworkimpactprinciples-to-practicesadoption
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

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