FIDES Technical Abstract
FIDES will enable interdisciplinary community convergence around data equity systems, with an initial study in critical domains such as mobility, housing, education, economic indicators, and government transparency, leading to the development of a novel data analytics infrastructure that supports responsibility in integrative data science. Towards this goal, the project will address several technically challenging problems:
- To be able to use data from multiple sources, risks related to privacy, bias, and the potential for misuse must be addressed. This project will develop principled methods for dataset processing to overcome these concerns.
- Individual datasets are difficult to integrate for use in advanced multi-layer network models. This project considers methods to create pre-trained tensors over large collections of spatially and temporally coherent datasets, making them easier to incorporate while controlling for fairness and equity.
- Any dataset or model must be equipped with sufficient information to determine fitness for use, communicate limitations, and describe underlying assumptions. This project will develop tools and techniques to produce "nutritional labels" for data and models, formalizing and standardizing ad hoc metadata approaches to provenance, specialized for equity issues. In addition to supporting methodological innovation in data science, the Institute will become a focal point for sharing expertise in data equity systems. It will do so by establishing interfaces for interaction between data science and domain experts to promote expertise development and sharing of best practices, and by consistently supporting efforts on diversity and equity.