Principles to Practices for Responsible AI: Closing the Gap

TL;DR

This paper analyzes the gap between high-level responsible AI principles and practical implementation, proposing an impact assessment framework to bridge this divide and illustrating its application through a forest restoration case study.

Contribution

It introduces a comprehensive, operationalizable impact assessment framework to help organizations implement responsible AI principles effectively.

Findings

The gap between principles and practices is due to disciplinary, tool, and organizational factors.

A broad, flexible, and participatory impact assessment framework can close this gap.

Case study demonstrates effective application of the framework in environmental AI use.

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.