Position: We Need Responsible, Application-Driven (RAD) AI Research
It addresses the problem of making AI research more relevant and ethical for society, but it is incremental as it builds on existing calls for responsible AI without introducing new technical breakthroughs.
This position paper argues that achieving meaningful advances in AI requires a responsible, application-driven approach (RAD-AI) that engages with specific contexts, ethical considerations, and societal constraints, proposing a three-staged method to drive research through transdisciplinary teams, context-specific methods, and staged testbeds.
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.