In this article, Herrie and Sørensen examine the mediation of typing indicators (“…”) in online messaging. Their point of departure is a scene from the contemporary novel Exciting Times by Naoise Dolan (2020), in which the ‘dots’ play a prominent role. Their analysis shows how typing indicators, as interface design, mediate the complex communication situation in which they take part: from being mere signals, they have slipped into our emotional lives. From a semiotic perspective (Charles S. Peirce), the authors define (...) typing indicators as uncertain indices which through unknowability and suspense establish an attentional presence. In continuation hereof, the authors argue that the acts of writing and waiting in contemporary attentional ecologies (Yves Citton) through the mediation of typing indicators as indicators of attentional presence, could be considered a mode of caring (Bernard Stiegler). (shrink)

Review of UNCERTAIN ARCHIVES: CRITICAL KEYWORDS FOR BIG DATA EDITED BY NANNA BONDE THYLSTRUP, DANIELA AGOSTINHO, ANNIE RING, CATHERINE D’IGNAZIO AND KRISTIN VEEL CAMBRIDGE: THE MIT PRESS, 2021. 640 PAGES ISBN: 9780262539883.

Machine learning systems have shown great potential for performing or supporting inferential reasoning through analyzing large data sets, thereby potentially facilitating more informed decision-making. However, a hindrance to such use of ML systems is that the predictive models created through ML are often complex, opaque, and poorly understood, even if the programs “learning” the models are simple, transparent, and well understood. ML models become difficult to trust, since lay-people, specialists, and even researchers have difficulties gauging the reasonableness, correctness, and reliability (...) of the inferences performed. In this article, we argue that bridging this gap in the understanding of ML models and their reasonableness requires a focus on developing an improved methodology for their creation. This process has been likened to “alchemy” and criticized for involving a large degree of “black art,” owing to its reliance on poorly understood “best practices”. We soften this critique and argue that the seeming arbitrariness often is the result of a lack of explicit hypothesizing stemming from an empiricist and myopic focus on optimizing for predictive performance rather than from an occult or mystical process. We present some of the problems resulting from the excessive focus on optimizing generalization performance at the cost of hypothesizing about the selection of data and biases. We suggest embedding ML in a general logic of scientific discovery similar to the one presented by Charles Sanders Peirce, and present a recontextualized version of Peirce’s scientific hypothesis adjusted to ML. (shrink)