Principles to Practices for Responsible AI: Closing the Gap
This addresses the challenge for companies and practitioners in operationalizing responsible AI, though it is incremental as it builds on existing principles.
The paper tackles the gap between high-level AI principles and their implementation in organizations, proposing an impact assessment framework as a solution and demonstrating its application through a case study on forest ecosystem restoration.
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.