<div><p>Part of the mission of the Data Science and AI Institute is to strengthen the reputation of Johns Hopkins as a premier source of trusted datasets that can be used for research, scholarship, and the development of trustworthy AI systems. As the NAIRR notes, there is a need for "trusted data providers and hosts for a transparent and responsible AI data commons. Access to data should be tiered, controlled by the data providers, and provided through the same portal through which computational resources are provided." Poor data practices can lead to a variety of problems including "bad" data, inherent biases, representational disparities, inability to generalize to real-world situations, and lack of robustness. In practical application, these problems can in turn lead to a host of consequences including miscarriage of justice, reputational damage, and liability.  </p><p>The Data Science and AI Institute invites projects that address the creation, documentation, quality assurance, and integrity of data used for the development of trustworthy AI systems.  We invite proposals for projects that address key elements of the data creation, documentation, and assurance challenge. Funding is available for two $25,000 projects in this fiscal year (FY25), with the expectation that additional resources at similar scale may be available in subsequent years. </p><p>Proposal submission deadline is Monday, Nov. 11.</p><p>For more information, click here.</p></div>

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Request for proposals: Data Science and AI Trusted Datasets

Part of the mission of the Data Science and AI Institute is to strengthen the reputation of Johns Hopkins as a premier source of trusted datasets that can be used for research, scholarship, and the development of trustworthy AI systems. As the NAIRR notes, there is a need for \"trusted data providers and hosts for a transparent and responsible AI data commons. Access to data should be tiered, controlled by the data providers, and provided through the same portal through which computational resources are provided.\" Poor data practices can lead to a variety of problems including \"bad\" data, inherent biases, representational disparities, inability to generalize to real-world situations, and lack of robustness. In practical application, these problems can in turn lead to a host of consequences including miscarriage of justice, reputational damage, and liability.  

Part of the mission of the Data Science and AI Institute is to strengthen the reputation of Johns Hopkins as a premier source of trusted datasets that can be used for research, scholarship, and the development of trustworthy AI systems. As the NAIRR notes, there is a need for "trusted data providers and hosts for a transparent and responsible AI data commons. Access to data should be tiered, controlled by the data providers, and provided through the same portal through which computational resources are provided." Poor data practices can lead to a variety of problems including "bad" data, inherent biases, representational disparities, inability to generalize to real-world situations, and lack of robustness. In practical application, these problems can in turn lead to a host of consequences including miscarriage of justice, reputational damage, and liability.  