We are developing data driven approaches to understanding inequality by integrating data across boundaries, including economic, healthcare, education, and both social and traditional media.
Moreover, data-driven decision making creates its own risks, for data that is based on previous decisions made by humans could contain our own systemic conscious and unconscious biases. With the increasing use of black box algorithms that produce decisions that cannot be easily explained, there is an urgent need for improved methodological approaches that address this, and for regulatory and ethical discussions to be made centrally important before these algorithms are deployed.