Principles to Practices for Responsible AI: Closing the Gap

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.

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)"

References in corpus (4)

• Principles alone cannot guarantee ethical AI
• AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
• Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study
• Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning

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• Developing and Evaluating a Design Method for Positive Artificial Intelligence