# Empowering Local Communities Using Artificial Intelligence

arXiv: https://arxiv.org/abs/2110.02007 · HTML: https://mlsanity.com/paper/2110.02007/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Envisioning Communities: A Participatory Approach Towards AI for Social Good](https://mlsanity.com/paper/2105.01774/) (2021)
- [Project RISE: Recognizing Industrial Smoke Emissions](https://mlsanity.com/paper/2005.06111/) (2020)
- [Community-Empowered Air Quality Monitoring System](https://mlsanity.com/paper/1804.03293/) (2018)
- [When Human-Computer Interaction Meets Community Citizen Science](https://mlsanity.com/paper/1907.11260/) (2019)
- [Smell Pittsburgh: Engaging Community Citizen Science for Air Quality](https://mlsanity.com/paper/1912.11936/) (2019)

## Built on by (most cited first, 7 of 7)

- [Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice](https://mlsanity.com/paper/2210.03709/) (2022)
- [Social Inclusion in Curated Contexts: Insights from Museum Practices](https://mlsanity.com/paper/2205.05192/) (2022)
- [FATE in AI: Towards Algorithmic Inclusivity and Accessibility](https://mlsanity.com/paper/2301.01590/) (2023)
- [CommunityAI: Towards Community-based Federated Learning](https://mlsanity.com/paper/2311.17958/) (2023)
- [Developing and Evaluating a Design Method for Positive Artificial Intelligence](https://mlsanity.com/paper/2402.01499/) (2024)
- [Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust](https://mlsanity.com/paper/2205.06920/) (2022)
- [Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets](https://mlsanity.com/paper/2608.22045/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) is increasingly used to analyze large amounts of data in various practices, such as object recognition. We are specifically interested in using AI-powered systems to engage local communities in developing plans or solutions for pressing societal and environmental concerns. Such local contexts often involve multiple stakeholders with different and even contradictory agendas, resulting in mismatched expectations of these systems' behaviors and desired outcomes. There is a need to investigate if AI models and pipelines can work as expected in different contexts through co-creation and field deployment. Based on case studies in co-creating AI-powered systems with local people, we explain challenges that require more attention and provide viable paths to bridge AI research with citizen needs. We advocate for developing new collaboration approaches and mindsets that are needed to co-create AI-powered systems in multi-stakeholder contexts to address local concerns.