# Envisioning Communities: A Participatory Approach Towards AI for Social Good

arXiv: https://arxiv.org/abs/2105.01774 · HTML: https://mlsanity.com/paper/2105.01774/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 89 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 13 of 13)

- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [Discrimination in Online Ad Delivery](https://mlsanity.com/paper/1301.6822/) (2013)
- [Roles for Computing in Social Change](https://mlsanity.com/paper/1912.04883/) (2019)
- [Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics](https://mlsanity.com/paper/2005.07572/) (2020)
- [Artificial Intelligence for Social Good: A Survey](https://mlsanity.com/paper/2001.01818/) (2020)
- [Towards decolonising computational sciences](https://mlsanity.com/paper/2009.14258/) (2020)
- [Data Science as Political Action: Grounding Data Science in a Politics of Justice](https://mlsanity.com/paper/1811.03435/) (2018)
- [Mechanism Design for Social Good](https://mlsanity.com/paper/1810.09832/) (2018)
- [Enabling Value Sensitive AI Systems through Participatory Design Fictions](https://mlsanity.com/paper/1912.07381/) (2019)
- [No computation without representation: Avoiding data and algorithm biases through diversity](https://mlsanity.com/paper/2002.11836/) (2020)
- [AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline](https://mlsanity.com/paper/2001.00088/) (2019)
- [Combating Anti-Blackness in the AI Community](https://mlsanity.com/paper/2006.16879/) (2020)

## Built on by (most cited first, 30 of 30)

- [The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset](https://mlsanity.com/paper/2303.03915/) (2023)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [In conversation with Artificial Intelligence: aligning language models with human values](https://mlsanity.com/paper/2209.00731/) (2022)
- [Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions](https://mlsanity.com/paper/2404.11023/) (2024)
- [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023)
- [Going public: the role of public participation approaches in commercial AI labs](https://mlsanity.com/paper/2306.09871/) (2023)
- [Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems](https://mlsanity.com/paper/2308.00868/) (2023)
- [Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits](https://mlsanity.com/paper/2403.14791/) (2024)
- [Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact](https://mlsanity.com/paper/2412.07880/) (2024)
- [Empowering Local Communities Using Artificial Intelligence](https://mlsanity.com/paper/2110.02007/) (2021)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance](https://mlsanity.com/paper/2408.01458/) (2024)
- [What Makes An Expert? Reviewing How ML Researchers Define "Expert"](https://mlsanity.com/paper/2411.00179/) (2024)
- [The Ethics of AI Value Chains](https://mlsanity.com/paper/2307.16787/) (2023)
- [The State of Computer Vision Research in Africa](https://mlsanity.com/paper/2401.11617/) (2024)
- [The Right to AI](https://mlsanity.com/paper/2501.17899/) (2025)
- [Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency](https://mlsanity.com/paper/2506.07281/) (2025)
- [Towards a Better Understanding of the Computer Vision Research Community in Africa](https://mlsanity.com/paper/2305.06773/) (2023)
- [Reflections from the Workshop on AI-Assisted Decision Making for Conservation](https://mlsanity.com/paper/2307.08774/) (2023)
- [Reflexive Prompt Engineering: A Framework for Responsible Prompt Engineering and Interaction Design](https://mlsanity.com/paper/2504.16204/) (2025)
- [The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation](https://mlsanity.com/paper/2602.11318/) (2026)
- [For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles](https://mlsanity.com/paper/2307.03764/) (2023)
- [Participation Interfaces for Human-Centered AI](https://mlsanity.com/paper/2211.08419/) (2022)
- [Why Algorithms Remain Unjust: Power Structures Surrounding Algorithmic Activity](https://mlsanity.com/paper/2405.18461/) (2024)
- [Social and Ethical Risks Posed by General-Purpose LLMs for Settling Newcomers in Canada](https://mlsanity.com/paper/2407.20240/) (2024)
- [RelAItionship Building: Analyzing Recruitment Strategies for Participatory AI](https://mlsanity.com/paper/2508.20176/) (2025)
- [From experimentation to engagement: on the paradox of participatory AI and power in contexts of forced displacement and humanitarian crises](https://mlsanity.com/paper/2604.06219/) (2026)
- [Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good](https://mlsanity.com/paper/2605.22995/) (2026)
- [A Cross Community Agenda for Speech AI](https://mlsanity.com/paper/2609.13168/) (2026)
- [Designing the Future of User Feedback for Generative AI](https://mlsanity.com/paper/2610.02631/) (2026)

## Abstract (arXiv)

Research in artificial intelligence (AI) for social good presupposes some definition of social good, but potential definitions have been seldom suggested and never agreed upon. The normative question of what AI for social good research should be "for" is not thoughtfully elaborated, or is frequently addressed with a utilitarian outlook that prioritizes the needs of the majority over those who have been historically marginalized, brushing aside realities of injustice and inequity. We argue that AI for social good ought to be assessed by the communities that the AI system will impact, using as a guide the capabilities approach, a framework to measure the ability of different policies to improve human welfare equity. Furthermore, we lay out how AI research has the potential to catalyze social progress by expanding and equalizing capabilities. We show how the capabilities approach aligns with a participatory approach for the design and implementation of AI for social good research in a framework we introduce called PACT, in which community members affected should be brought in as partners and their input prioritized throughout the project. We conclude by providing an incomplete set of guiding questions for carrying out such participatory AI research in a way that elicits and respects a community's own definition of social good.