To fail an asylum seeker: Time, space and legal events

Legal geographers have recently highlighted the importance of attending to the interaction of time and space to understand law and its enactment. We build on these efforts to examine the spatiotemporal influences over the processes by which asylum claim determination procedures in Western industrialised countries seek to reconstruct past events for the purposes of deciding refugee claims. Two ‘common-sense’ beliefs underpin this reconstruction: that the occurrences leading to a fear of persecution can be isolated and that the veracity of an asylum claim is objectively independent from the process of uncovering it. We critically interrogate these assumptions by conceptualising the fears of people seeking asylum as Deleuzian ‘events’. Basing our argument on 41 interviews with people who have previously claimed asylum in the United Kingdom and firsthand accounts of asylum appeals, we explore the folding together of asylum ‘truths’ and the spatiotemporal processes by which they are arrived at, arguing that refused asylum claims are not simply detected by the process – they are produced by it.

Using machine learning to correct model error in data assimilation and forecast applications

<p>Recent developments in machine learning (ML) have demonstrated impressive skills in reproducing complex spatiotemporal processes. However, contrary to data assimilation (DA), the underlying assumption behind ML methods is that the system is fully observed and without noise, which is rarely the case in numerical weather prediction. In order to circumvent this issue, it is possible to embed the ML problem into a DA formalism characterised by a cost function similar to that of the weak-constraint 4D-Var (Bocquet et al., 2019; Bocquet et al., 2020). In practice ML and DA are combined to solve the problem: DA is used to estimate the state of the system while ML is used to estimate the full model. </p><p>In realistic systems, the model dynamics can be very complex and it may not be possible to reconstruct it from scratch. An alternative could be to learn the model error of an already existent model using the same approach combining DA and ML. In this presentation, we test the feasibility of this method using a quasi geostrophic (QG) model. After a brief description of the QG model model, we introduce a realistic model error to be learnt. We then asses the potential of ML methods to reconstruct this model error, first with perfect (full and noiseless) observation and then with sparse and noisy observations. We show in either case to what extent the trained ML models correct the mid-term forecasts. Finally, we show how the trained ML models can be used in a DA system and to what extent they correct the analysis.</p><p>Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models, Nonlin. Processes Geophys., 26, 143–162, 2019</p><p>Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization, Foundations of Data Science, 2 (1), 55-80, 2020</p><p>Farchi, A., Laloyaux, P., Bonavita, M., and Bocquet, M.: Using machine learning to correct model error in data assimilation and forecast applications, arxiv:2010.12605, submitted. </p>

Algorithms as organizational figuration: The sociotechnical arrangements of a fintech start-up

Building on critical approaches that understand algorithms in terms of communication, culture and organization, this paper offers the supplementary conceptualization of algorithms as organizational figuration, defined as material and meaningful sociotechnical arrangements that develop in spatiotemporal processes and are shaped by multiple enactments of affordance–agency relations. We develop this conceptualization through a case study of a Danish fintech start-up that uses machine learning to create opportunities for sustainable pensions investments. By way of ethnographic and literary methodology, we provide an in-depth analysis of the dynamic trajectory in and through which the organization gives shape to and takes shape from its key algorithmic tool, mapping the shifting sociotechnical arrangements of the start-up, from its initial search for a viable business model through the development of the algorithm to the public launch of its product. On this basis, we argue that conceptualizing algorithms as organizational figuration enables us to detail not only what algorithms do but also what they are.