The lecture series on “Data Practices” explores data “in motion”, both theoretically, empirically and methodology. The proliferation of data-intensive media requires researchers to develop their conceptual vocabulary and socio-technical understanding of data production, calculation and their underlying practices and infrastructures. Throughout the lecture series, we ask how a praxeological account can enable us to account for the movement and transformation of data. We consider data practices as those practices involved in the making, calculation, storage, accounting and valuation of data among others which are socio-material and entangled with infrastructures. The lecture series is jointly organised by the DFG graduate school “Locating Media” and the DFG cooperative research centre “Media of Cooperation”.
Dr. Marcus Burkhardt on “Learning in the wild: on the problem of adaptivity in machine learning”
In June 2017 Sundar Pichai, CEO of Google, proposed a paradigm shift in the history of computing: Innovation should neither be driven by approaching problems as first and foremost digital nor mobile, but instead by taking an AI first approach that is fueled by recent advances in the field of machine learning. This statement reflects a central promise of machine learning applications, namely the ability to adapt to unforeseen futures without being explicitly programmed: visual recognition of objects or persons without ever having seen or trained on this specific object or this specific person before, self-driving cars being able to deal with new situations safely or chatbots conducting conversations with humans in an engaging manner.
Conversely, the more such technologies are built into the fabric of everyday life the more concerns are raised about their potential risks, e.g. biases and inequalities inherent in training data sets. As a result, ML models often produce (social) structures instead of adapting to them. This tension between promises of ML and perceived risks points toward a hitherto largely unstudied aspect of data-driven applications: the production of adaptivity in real-world ML applications. Drawing on examples like Microsoft's chatbot Tay.ai, recommender systems and fraud detection applications the paper aims to unpack the notions of adaptivity that ML rests upon. By focusing on how adaptivity is accomplished on different levels and to varying extents our goal is to explore the ontological politics that ML systems enact in the wild of their real-world deployment.
AH - A 217/18