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”.
Louise Amoore (Durham) on "Cloud Ethics: Algorithms and the Attributes of Others"
The techniques deployed in deep neural net algorithms to condense the features of a scene to an output of meaning – “a man is throwing a Frisbee in a park”, “a woman is standing at the border fence with a crowd in the background” – give an account of the ethico-politics of algorithms for our times. The output of the algorithms reduces the intractable difficulties and duress of living, the undecidability of what could be happening in a scene, into a single human-readable and actionable meaning. We have ethical and political relationships with other beings in the world because the meaning of those relations, their mediation through every scene of life, cannot be condensed. It is precisely irreducible. And so, at the very moment that the algorithm outputs a single meaning from an irreducible scene, there is also at this border limit a “clause of nonclosure”, as Derrida describes the opening of context. How does one begin to locate the points of nonclosure within the algorithm’s programme of meaning-making? In contrast to the widespread search for ethical limits of the actions of algorithms, I propose a cloud ethics that is concerned with the formation of relations to oneself and to others. Are there counter-methods of attention available to us that could resist the frameworks of attention of machine learning? Amid the technologies of the attribute, what remains of that which is unattributable in the scene?
AH - A 217/18