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Data Ethics Conversation Series: Sabina Leonelli
Data Science in Times of Pan(dem)ic: or, From FAIR Data to Fair Data Use

This talks starts with considering a timely question: what are the priorities for data science in tackling COVID-19 and in which ways can big data analysis inform and support responses to the outbreak? I argue that it is imperative for data scientists to spend time and resources scoping, scrutinizing and questioning the possible scenarios of use of their work – particularly given the fast-paced knowledge production required by an emergency situation such as the coronavirus pandemic. I provide a scaffold for such considerations by identifying five ways in which the data science contributions to the pandemic response are imagined and projected into the future, and reflecting on how such, imaginaries inform current allocations of investment and priorities within and beyond the scientific research landscape.

To underscore these points and test their validity beyond the emergency context provided by the current pandemic, I then shift to a discussion of what I call “methodological data fairness”. Attention to this understanding of epistemic fairness in relation to the use of data in research complements current attention to the FAIR, CARE and TRUST principles within data science, as I briefly exemplify in the case of social media data use for public health research. I conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological, and epistemic issues with the knowledge and evidence that are being produced, both in relation to current outbreak responses and beyond.

Dec 9, 2020 11:00 AM in Pacific Time (US and Canada)

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