# The Limits of Global Inclusion in AI Development

arXiv: https://arxiv.org/abs/2102.01265 · HTML: https://mlsanity.com/paper/2102.01265/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2021.

## Built on by (most cited first, 14 of 14)

- [Going public: the role of public participation approaches in commercial AI labs](https://mlsanity.com/paper/2306.09871/) (2023)
- [Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models](https://mlsanity.com/paper/2311.05746/) (2023)
- [Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost](https://mlsanity.com/paper/2403.07687/) (2024)
- [Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice](https://mlsanity.com/paper/2506.09873/) (2025)
- [A Taxonomy of Challenges to Curating Fair Datasets](https://mlsanity.com/paper/2406.06407/) (2024)
- [Towards a Praxis for Intercultural Ethics in Explainable AI](https://mlsanity.com/paper/2304.11861/) (2023)
- [Participation Interfaces for Human-Centered AI](https://mlsanity.com/paper/2211.08419/) (2022)
- [Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders](https://mlsanity.com/paper/2304.06031/) (2023)
- [Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems](https://mlsanity.com/paper/2401.13097/) (2024)
- [Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobia](https://mlsanity.com/paper/2504.13085/) (2025)
- ["Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessness](https://mlsanity.com/paper/2508.13187/) (2025)
- [Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategies](https://mlsanity.com/paper/2602.08187/) (2026)
- [Position: Ideas Should be the Center of Machine Learning Research](https://mlsanity.com/paper/2605.15253/) (2026)
- [Whose fairness? Structural concentration in AI bias research](https://mlsanity.com/paper/2607.05574/) (2026)

## Abstract (arXiv)

Those best-positioned to profit from the proliferation of artificial intelligence (AI) systems are those with the most economic power. Extant global inequality has motivated Western institutions to involve more diverse groups in the development and application of AI systems, including hiring foreign labour and establishing extra-national data centers and laboratories. However, given both the propensity of wealth to abet its own accumulation and the lack of contextual knowledge in top-down AI solutions, we argue that more focus should be placed on the redistribution of power, rather than just on including underrepresented groups. Unless more is done to ensure that opportunities to lead AI development are distributed justly, the future may hold only AI systems which are unsuited to their conditions of application, and exacerbate inequality.