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

arXiv: https://arxiv.org/abs/2505.04104 · HTML: https://mlsanity.com/paper/2505.04104/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 62 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 7 of 7)

- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [Promises and Pitfalls of Black-Box Concept Learning Models](https://mlsanity.com/paper/2106.13314/) (2021)
- [Application-Driven Innovation in Machine Learning](https://mlsanity.com/paper/2403.17381/) (2024)
- [Collaborative human-AI trust (CHAI-T): A process framework for active management of trust in human-AI collaboration](https://mlsanity.com/paper/2404.01615/) (2024)
- [ABC Align: Large Language Model Alignment for Safety & Accuracy](https://mlsanity.com/paper/2408.00307/) (2024)
- [Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems](https://mlsanity.com/paper/2402.08193/) (2024)
- [A Neural Emulator for Uncertainty Estimation of Fire Propagation](https://mlsanity.com/paper/2305.06139/) (2023)

## Built on by (most cited first, 2 of 2)

- [An Investigation of the NeurIPS and ICML 2025 Position Tracks](https://mlsanity.com/paper/2608.16894/) (2026)
- [Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research](https://mlsanity.com/paper/2610.01116/) (2026)

## Abstract (arXiv)

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.