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ABSTRACT: While major funding agencies are striving for diversity and fairness, the mechanisms behind funding success have yet to be fully elucidated. Existing studies reveal valuable evidences about the effect of the applicant's individual attributes, e.g., gender and age, on the funding success. However, the relationship between funding success and academic activities, e.g., collaborator's characteristics, remains underexplored. This work collects massive scholarly data from open academic graphs and public data about National Institutes of Health awards to investigate the effect of various academic graph‐based factors on the “K to R” success. Leveraging a heterogeneous graph model for predicting the “K to R” success, we regard the gain in the model performance of a factor as a proxy variable for the magnitude of its effect on the “K to R” success. Our preliminary results suggest that interest by peers in the applicant's research and the timing of the interest are strongly correlated with the outcome. Meanwhile, the applicant's social connections, e.g., their collaborators, can also contribute to the outcome. 

ABSTRACT Artificial Intelligence (AI) methods are valued for their ability to predict outcomes from dynamically complex data. Despite this virtue, AI is widely criticized as a “black box” i.e., lacking mechanistic explanations to accompany predictions. We introduce a novel interdisciplinary approach that balances the predictive power of data-driven methods with theory-driven explanatory power by presenting a shared use case from four disciplinary perspectives. The use case examines scientific career trajectories through temporally complex, heterogeneous bibliographic big data. Topics addressed include: data representation in complex problems, trade-offs between theoretical, hypothesis driven, and data-driven approaches, AI trustworthiness, model fairness, algorithm explainability and AI adoption/usability. Panelists and audience members will be prompted to discuss the value of approach presented versus other ways to address the challenges raised by the panel, and to consider their limitations and remaining challenges.