This talk expands on a recent series of studies asserting that algorithms – be they associated with terms such as “big data,” “machine learning,” or “artificial intelligence” (AI) – derive ultimately from benchmark datasets, often called ground truths, that gather input-data and output-targets, thereby establishing what can be retrieved computationally and evaluated statistically. I explore the case of the Tumor nEoantigen SeLection Alliance (TESLA), a consortium-based ground-truthing project in personalized cancer immunotherapy, where the “truth” of the targets to be retrieved by the would-be AI algorithms – immunogenic neoantigens – depended upon a broad technoscientific network whose setting up implied important organizational and material infrastructures. Moreover, instead of grounding a confident “truth,” the TESLA endeavor ended up establishing a contested reference, the biology of immunogenic neoantigens having slightly evolved during the lapse of this four-year project. More generally, this case study indicates that the enforcement of ground truths, and what it leaves out, is a necessary condition to enable AI in personalized medicine.

Florian Jaton is Postdoctoral Researcher at the STS Lab, a research unit of the Institute of Social Sciences of the University of Lausanne, Switzerland. Florian studied Philosophy, Mathematics, Literature, and Political Sciences before receiving his PhD in Social Sciences at the University of Lausanne. He also worked at the Donald Bren School of Information and Computer Science at the University of California Irvine and at the Centre de Sociologie de l’Innovation at the École des Mines de Paris. His research interests are the sociology of algorithms, the philosophy of mathematics, and the history of computing. He is the author of The Constitution of Algorithms: Ground-Truthing, Programming, Formulating (MIT Press, 2021; open access).

The event takes place on zoom.

Zoom-Link:

https://hu-berlin.zoom.us/j/68771679884?pwd=OENiTGloRzdpcmx3c2EyWHlQdmJJQT09

Meeting-ID: 687 7167 9884

Password: 760565