Deadline: 27-Jul-21

The Lacuna Fund is seeking Expressions of Interest (EOIs) from multi-disciplinary teams to develop open and accessible training and evaluation datasets for ML applications that address inequities in healthcare outcomes in the United States and in Low and Middle-Income Countries (LMICs) globally.

The goal is to support the creation, augmentation, or aggregation of datasets that are representative of affected populations and are therefore less biased and more likely to lead to equitable health outcomes.

Given historic inequities related to race in the U.S., they are interested in datasets that could help reduce racial disparities in healthcare outcomes in the U.S., and datasets that can mitigate inequities in healthcare outcomes related to identity in LMICs (e.g. ethnicity, tribal affiliation, gender, etc.).


The Fund Technical Advisory Panel, which is responsible for identifying data gaps, developing the EOI, and reviewing and selecting proposals, has identified needs for datasets that can be used to address a health disparity in the following areas:

  • cancer
  • infectious disease
  • chronic disease

Eligibility Criteria

Lacuna Fund aims to make its funding accessible to as many organizations as possible in the AI for social good space and cultivate capacity and emerging organizations in the field.

To be eligible for funding, organizations must:

  • Be either a non-profit entity, research institution, for-profit social enterprise, or a team of such organizations.
  • Individuals must apply through an institutional sponsor.
  • Partnerships are welcome but only the lead applicant will receive funds.
  • Have a mission supporting societal good, broadly defined.
  • Have all necessary national or other approvals to conduct proposed research.
  • The approval process may be conducted in parallel with grant application, if necessary.
  • Approval costs, if any, are the responsibility of the applicant.
  • Have the technical capacity to conduct dataset labeling, creation, expansion, and/or maintenance.
  • Be sufficiently stable to ensure the sustainability of the dataset or have arrangements with an organization of sufficient stability.

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