# The Hardness of Achieving Impact in AI for Social Impact Research: A Ground-Level View of Challenges & Opportunities

arXiv: https://arxiv.org/abs/2506.14829 · HTML: https://mlsanity.com/paper/2506.14829/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 89 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Exploring Algorithmic Fairness in Robust Graph Covering Problems](https://mlsanity.com/paper/2006.06865/) (2020)
- [Towards Equity and Algorithmic Fairness in Student Grade Prediction](https://mlsanity.com/paper/2105.06604/) (2021)
- [Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers](https://mlsanity.com/paper/2409.08916/) (2024)
- [Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness](https://mlsanity.com/paper/2009.09559/) (2020)
- [AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development](https://mlsanity.com/paper/2502.18682/) (2025)
- [Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version](https://mlsanity.com/paper/1602.00165/) (2016)
- [Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact](https://mlsanity.com/paper/2412.07880/) (2024)
- [Becoming Good at AI for Good](https://mlsanity.com/paper/2104.11757/) (2021)
- [AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline](https://mlsanity.com/paper/2001.00088/) (2019)
- [CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback](https://mlsanity.com/paper/2411.09073/) (2024)
- [AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing](https://mlsanity.com/paper/1810.12847/) (2018)
- [Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey](https://mlsanity.com/paper/2410.18390/) (2024)
- [Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance](https://mlsanity.com/paper/2403.12599/) (2024)
- ["Come to us first": Centering Community Organizations in Artificial Intelligence for Social Good Partnerships](https://mlsanity.com/paper/2409.06814/) (2024)
- [Edge AI for Real-time Fetal Assessment in Rural Guatemala](https://mlsanity.com/paper/2503.09659/) (2025)
- [Activating the "Breakfast Club": Modeling Influence Spread in Natural-World Social Networks](https://mlsanity.com/paper/1710.00364/) (2017)

## Built on by (most cited first, 1 of 1)

- [When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital](https://mlsanity.com/paper/2508.08504/) (2025)

## Abstract (arXiv)

AI for Social Impact (AI4SI) is an emergent field harnessing interdisciplinarities between the fields of artificial intelligence (AI), machine learning (ML), and the social sciences to address societal issues aligned with the United Nations Sustainable Development Goals (UN SDGs), such as universal healthcare, climate action, etc. Despite AI4SI's rising popularity, achieving tangible, on-the-ground impact remains a significant challenge. In particular, identifying collaborators open to co-designing and deploying AI4SI-based solutions in real-world settings is often difficult. Thus, many projects stall at the proof-of-concept stage, unable to scale to production-level deployment. Drawing on twenty-six AI4SI researchers' interviews, primarily from academic institutions though also including some industry researchers and practitioners, and the authors' own lived experiences, this paper employs thematic analysis to highlight structural, organizational, communication, collaboration, and operational challenges hindering socially impactful AI4SI deployments. While there are no easy fixes, the authors synthesize best practices and actionable strategies from interviews and personal experiences, positioning this paper as a practical guide for AI4SI researchers and organizations pursuing socially impactful collaborations1. 1We note that our findings are most directly applicable to academic research groups in the global north, as governmental, startup, and global south researchers' perspectives are underrepresented in our sample.