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This paper advocates for inclusive AI development, emphasizing its necessity for global equity, ethical soundness, and social relevance. We detail MIT Critical Data's approach to equitable AI development, focusing on healthcare. Our methods prioritize diverse collaboration and community engagement. Through global datathons, open-source datasets, and accessible education, we empower the global majority to actively participate in shaping AI that benefits all. Significant results, including numerous publications and established community hubs, demonstrate the impact of this approach. We argue that inclusivity in AI is not only achievable but crucial for its future success and fairness, particularly in serving the global majority.

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From AI Bias to AI by Us: A Case Study from MIT Critical Data

  • 会议论文

  • 发表日期：

    2026年

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  • Catherine Bielick,
  • Rodrigo Gameiro,
  • Leo Celi

摘要

This paper advocates for inclusive AI development, emphasizing its necessity for global equity, ethical soundness, and social relevance. We detail MIT Critical Data's approach to equitable AI development, focusing on healthcare. Our methods prioritize diverse collaboration and community engagement. Through global datathons, open-source datasets, and accessible education, we empower the global majority to actively participate in shaping AI that benefits all. Significant results, including numerous publications and established community hubs, demonstrate the impact of this approach. We argue that inclusivity in AI is not only achievable but crucial for its future success and fairness, particularly in serving the global majority.