Position: We Need Responsible, Application-Driven (RAD) AI Research

TL;DR

This paper advocates for a responsible, application-driven AI research approach that emphasizes ethical, societal, and contextual considerations to ensure AI advances are meaningful and beneficial for society.

Contribution

It introduces a three-stage framework for RAD-AI, emphasizing transdisciplinary teams, context-specific methods, and staged testing to align AI research with societal needs.

Findings

Promotes transdisciplinary, people-centered AI research

Highlights importance of context-specific ethical considerations

Proposes staged testing and community engagement for sustainable AI development

Abstract

This position paper argues that achieving meaningful scientific and societal advances with artificial intelligence (AI) requires a responsible, application-driven approach (RAD) to AI research. As AI is increasingly integrated into society, AI researchers must engage with the specific contexts where AI is being applied. This includes being responsive to ethical and legal considerations, technical and societal constraints, and public discourse. We present the case for RAD-AI to drive research through a three-staged approach: (1) building transdisciplinary teams and people-centred studies; (2) addressing context-specific methods, ethical commitments, assumptions, and metrics; and (3) testing and sustaining efficacy through staged testbeds and a community of practice. We present a vision for the future of application-driven AI research to unlock new value through technically feasible methods that are adaptive to the contextual needs and values of the communities they ultimately serve.