All Together Now: Data Work to Advance Privacy, Science, and Health in the Age of Synthetic Data

There is a disconnect between data practices in biomedicine and public understanding of those data practices, and this disconnect is expanding rapidly every day (with the emergence of synthetic data and digital twins and more widely adopted Artificial Intelligence (AI)/Machine Learning tools). Transparency alone is insufficient to bridge this gap. Concurrently, there is an increasingly complex landscape of laws, regulations, and institutional/ programmatic policies to navigate when engaged in biocomputing and digital health research, which makes it increasingly difficult for those wanting to "get it right" or "do the right thing." Mandatory data protection obligations vary widely, sometimes focused on the type of data (and nuanced definition and scope parameters), the actor/entity involved, or the residency of the data subjects. Additional challenges come from attempts to celebrate biocomputing discoveries and digital health innovations, which frequently transform fair and accurate communications into exaggerated hype (e.g., to secure financial investment in future projects or lead to more favorable tenure and promotion decisions). Trust in scientists and scientific expertise can be quickly eroded if, for example, synthetic data is perceived by the public as "fake data" or if digital twins are perceived as "imaginary" patients. Researchers appear increasingly aware of the scientific and moral imperative to strengthen their work and facilitate its sustainability through increased diversity and community engagement. Moreover, there is a growing appreciation for the "data work" necessary to have scientific data become meaningful, actionable information, knowledge, and wisdom-not only for scientists but also for the individuals from whom those data were derived or to whom those data relate. Equity in the process of biocomputing and equity in the distribution of benefits and burdens of biocomputing both demand ongoing development, implementation, and refinement of embedded Ethical, Legal and Social Implications (ELSI) research practices. This workshop is intended to nurture interdisciplinary discussion of these issues and to highlight the skills and competencies all too often considered "soft skills" peripheral to other skills prioritized in traditional training and professional development programs. Data scientists attending this workshop will become better equipped to embed ELSI practices into their research.