Niccolò Tempini is Senior Lecturer in Data Studies at the University of Exeter, Department of Sociology, Philosophy and Anthropology, and a Turing Fellow at The Alan Turing Institute. He is an interdisciplinary social scientist interested in questions of information, data, technology, organisation, value and knowledge. He researches big data research and digital infrastructures, investigating the specific knowledge production economies, organisation forms and data management innovations that these projects engender with a focus in their social and epistemic consequences.
He studies the practices of data scientists, software developers, researchers and non-professionalised experts, to understand how different forms of knowledge and value intersect with each other when different actors come to grips with new methods and new forms of data, information technology and organisation. His research has been published in international journals across science and technology studies, information systems, sociology and philosophy. More information at www.tempini.info.
For a few years, Niccolò has been investigating the ways in which people and institutions use data and information technology to know something about the social and personal life of a population or an individual, and what organisational trade-offs and social consequences each innovative approach presents. Issues related to the value, management, control, governance and security of data and Internet networks are today at the centre of some of the most current debates in the public sphere. Niccolò has been studying, among others, various issues connected to the collection, governance and reuse of data such as change in work roles and problem domain; the organisation of interdisciplinary research projects; the shifting boundaries of formal organisations and institutions, and the associated issues of trust, accountability and security; and changes in the value, usability and meaningfulness of data as they move across situations.
His future research will focus on the social and epistemic conditions in machine learning development for health care and research; and in the design of 'soft' methodologies for ML technology design and development. Cutting edge data innovations succeed when they sit at the crossroads of complex convergences of heterogeneous requirements (interdisciplinary, communicational, methodological, social, ethical).
In this research Niccolò will aim to observe and explain how machine learning is introduced in contexts of health care and research, and the gap between development and adoption. He will aim to develop a “methodological toolkit” that can help to factor social and contextual conditions in machine learning technology development.