Empowering Local Communities Using Artificial Intelligence
Not on the map. Why?
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 9 of its 58 references that are in the map. First posted on arXiv 5 Oct 2021.
Builds on
9 of 58 references.
References · 9
- 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
- 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
- 2020Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scanscited by 158
- 2020Participation is not a Design Fix for Machine Learningcited by 110
- 2021Envisioning Communities: A Participatory Approach Towards AI for Social Goodcited by 29
- 2020Project RISE: Recognizing Industrial Smoke Emissionscited by 5
- 2018Community-Empowered Air Quality Monitoring Systemcited by 2
- 2019When Human-Computer Interaction Meets Community Citizen Sciencecited by 1
- 2019Smell Pittsburgh: Engaging Community Citizen Science for Air Qualitycited by 1
Built on by
Citing papers per year: 2022: 3; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
7 papers.
- 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 30
- 2022Social Inclusion in Curated Contexts: Insights from Museum Practicescited by 7
- 2023FATE in AI: Towards Algorithmic Inclusivity and Accessibilitycited by 3
- 2023CommunityAI: Towards Community-based Federated Learningcited by 2
- 2024Developing and Evaluating a Design Method for Positive Artificial Intelligencecited by 2
- 2022Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trustcited by 1
- 2026Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasetscited by 0
- 2026Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets22 Aug 2026
- 2024Developing and Evaluating a Design Method for Positive Artificial Intelligence2 Feb 2024
- 2023CommunityAI: Towards Community-based Federated Learning29 Nov 2023
- 2023FATE in AI: Towards Algorithmic Inclusivity and Accessibility3 Jan 2023
- 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice7 Oct 2022
- 2022Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust13 May 2022
- 2022Social Inclusion in Curated Contexts: Insights from Museum Practices10 May 2022