Description: Recent work within the HCI and AI research communities has highlighted the significant impact of uncertainties on decision-making in any high-stakes domain. There is an irreducible degree of uncertainty associated with any predicted decision which has important implications, especially for algorithmic decision-making in the public sector where mistakes can have serious consequences for the lives of citizens [1,2,3]. Consequently, researchers have highlighted the need to uncover important sources of uncertainties because it is at these points that human discretion is most needed as well as allow for a closer inspection of confounding factors that can lead to erroneous decisions from both algorithmic systems and human decision-makers [3]. In the child-welfare system, caseworkers are trained to write detailed casenotes which encapsulate critical decisions, circumstances surrounding these decisions, families’ interactions in their social ecosystem, uncertainties in a case, as well as competing factors that confound caseworkers’ judgments. As illustrated by our recent study on computational narrative analysis of casenotes, these contextual factors can be derived from this unstructured text and can help inform decision-making. In this next study, we will employ transformer-based NLP models such as BERT to inspect casenotes and assess how different risk, protective, and systemic factors mediate one another through the life of a child-welfare case, how uncertainties arise (at the intersection of these factors), and finally, how these critical insights can be meaningfully presented to a child-welfare team such that it informs a collaborative decision-making process