Making Responsible AI the Norm rather than the Exception

TL;DR

This paper proposes an actionable framework to operationalize Responsible AI principles, aiming to embed responsible practices into standard workflows and decision-making processes across AI development.

Contribution

It introduces a comprehensive framework combining knowledge exchange, risk prioritization, and complexity management to make Responsible AI the norm rather than an exception.

Findings

Framework facilitates practical implementation of Responsible AI principles

Empirically-driven risk prioritization guides responsible development

Components reinforce each other to embed responsibility in AI workflows

Abstract

This report prepared by the Montreal AI Ethics Institute provides recommendations in response to the National Security Commission on Artificial Intelligence (NSCAI) Key Considerations for Responsible Development and Fielding of Artificial Intelligence document. The report centres on the idea that Responsible AI should be made the Norm rather than an Exception. It does so by utilizing the guiding principles of: (1) alleviating friction in existing workflows, (2) empowering stakeholders to get buy-in, and (3) conducting an effective translation of abstract standards into actionable engineering practices. After providing some overarching comments on the document from the NSCAI, the report dives into the primary contribution of an actionable framework to help operationalize the ideas presented in the document from the NSCAI. The framework consists of: (1) a learning, knowledge, and information exchange (LKIE), (2) the Three Ways of Responsible AI, (3) an empirically-driven risk-prioritization matrix, and (4) achieving the right level of complexity. All components reinforce each other to move from principles to practice in service of making Responsible AI the norm rather than the exception.