Position: We Need Responsible, Application-Driven (RAD) AI Research
Authors: Sarah Hartman, Cheng Soon Ong, Julia Powles, Petra Kuhnert
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. ... 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... |
| Researcher Affiliation | Academia | 1CSIRO s Data61, Australia 2College of Systems and Society, Australian National University, Australia 3Tech & Policy Lab, Law School, University of Western Australia, Australia 4Centre for Business Research, University of Cambridge, United Kingdom. Correspondence to: Sarah Hartman <EMAIL>. |
| Pseudocode | No | The paper describes conceptual approaches and frameworks (e.g., 'A Three-staged Approach to RAD-AI') but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses a conceptual framework for responsible AI research and does not describe a specific methodology for which source code would be released. There are no statements about code availability or links to repositories. |
| Open Datasets | No | The paper is a position paper outlining a framework for AI research and does not present experiments that use specific datasets. It does not provide any concrete access information (link, DOI, repository, or citation with author/year) for publicly available or open datasets. |
| Dataset Splits | No | The paper does not present experimental results or use specific datasets, therefore, it does not provide information on training/test/validation dataset splits. |
| Hardware Specification | No | This position paper does not describe any specific experiments or computational work, and therefore does not provide details about hardware specifications. |
| Software Dependencies | No | This position paper does not describe any specific experiments or computational work, and therefore does not provide details about software dependencies with version numbers. |
| Experiment Setup | No | This position paper outlines a conceptual framework for responsible AI research and does not present any specific experimental setup details, hyperparameters, or training configurations. |