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

Sarah Hartman, Cheng Soon Ong, Julia Powles, Petra Kuhnert
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:81514-81525, 2025.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-hartman25a, title = {Position: We Need Responsible, Application-Driven ({RAD}) {AI} Research}, author = {Hartman, Sarah and Ong, Cheng Soon and Powles, Julia and Kuhnert, Petra}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {81514--81525}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/hartman25a/hartman25a.pdf}, url = {https://proceedings.mlr.press/v267/hartman25a.html}, 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.} }
Endnote
%0 Conference Paper %T Position: We Need Responsible, Application-Driven (RAD) AI Research %A Sarah Hartman %A Cheng Soon Ong %A Julia Powles %A Petra Kuhnert %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-hartman25a %I PMLR %P 81514--81525 %U https://proceedings.mlr.press/v267/hartman25a.html %V 267 %X 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.
APA
Hartman, S., Ong, C.S., Powles, J. & Kuhnert, P.. (2025). Position: We Need Responsible, Application-Driven (RAD) AI Research. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:81514-81525 Available from https://proceedings.mlr.press/v267/hartman25a.html.

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