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There has been increasing concern within the machine learning community and beyond that Artificial Intelligence (AI) faces a bias and discrimination crisis which needs AI fairness with urgency. As many have begun to work on this problem, most existing work depends on the availability of class label for the given fairness definition and algorithm which may not align with real-world usage. In this work, we study an AI fairness problem that stems from the gap between the design of a fair model in the lab and its deployment in the real-world. Specifically, we consider defining and mitigating individual unfairness amidst censorship, where the availability of class label is not always guaranteed due to censorship, which is broadly applicable in a diversity of real-world socially sensitive applications. We show that our method is able to quantify and mitigate individual unfairness in the presence of censorship across three benchmark tasks, which provides the first known results on individual fairness guarantee in analysis of censored data. 

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.