The Limits of Global Inclusion in AI Development
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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.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Feb 2021.
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Citing papers per year: 2022: 1; 2023: 4; 2024: 3; 2025: 3; 2026: 3.
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- 2023Going public: the role of public participation approaches in commercial AI labscited by 11
- 2023Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Modelscited by 11
- 2024Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Costcited by 5
- 2025Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practicecited by 5
- 2024A Taxonomy of Challenges to Curating Fair Datasetscited by 2
- 2023Towards a Praxis for Intercultural Ethics in Explainable AIcited by 1
- 2022Participation Interfaces for Human-Centered AIcited by 0
- 2023Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholderscited by 0
- 2024Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systemscited by 0
- 2025Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobiacited by 0
- 2025"Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessnesscited by 0
- 2026Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategiescited by 0
2 more
- 2026Whose fairness? Structural concentration in AI bias research6 Jul 2026
- 2026Position: Ideas Should be the Center of Machine Learning Research14 May 2026
- 2026Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategies9 Feb 2026
- 2025"Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessness14 Aug 2025
- 2025Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice11 Jun 2025
- 2025Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobia17 Apr 2025
- 2024A Taxonomy of Challenges to Curating Fair Datasets10 Jun 2024
- 2024Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost12 Mar 2024
- 2024Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems23 Jan 2024
- 2023Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models9 Nov 2023
- 2023Going public: the role of public participation approaches in commercial AI labs16 Jun 2023
- 2023Towards a Praxis for Intercultural Ethics in Explainable AI24 Apr 2023
- 2023Going public: the role of public participation approaches in commercial AI labscited by 11
- 2024Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Costcited by 5
- 2025Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practicecited by 5
- 2024A Taxonomy of Challenges to Curating Fair Datasetscited by 2
- 2023Towards a Praxis for Intercultural Ethics in Explainable AIcited by 1
- 2022Participation Interfaces for Human-Centered AIcited by 0
- 2023Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholderscited by 0
- 2024Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systemscited by 0
- 2025Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobiacited by 0
- 2025"Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessnesscited by 0
- 2026Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategiescited by 0
- 2026Position: Ideas Should be the Center of Machine Learning Researchcited by 0
12 most cited of 13 All 13 (JSON)
- 2026Whose fairness? Structural concentration in AI bias research6 Jul 2026
- 2026Position: Ideas Should be the Center of Machine Learning Research14 May 2026
- 2026Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategies9 Feb 2026
- 2025"Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessness14 Aug 2025
- 2025Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice11 Jun 2025
- 2025Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobia17 Apr 2025
- 2024A Taxonomy of Challenges to Curating Fair Datasets10 Jun 2024
- 2024Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost12 Mar 2024
- 2024Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems23 Jan 2024
- 2023Going public: the role of public participation approaches in commercial AI labs16 Jun 2023
- 2023Towards a Praxis for Intercultural Ethics in Explainable AI24 Apr 2023
- 2023Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders22 Mar 2023
12 newest of 13 All 13 (JSON)