.... this is a demo to showcase how the chronodiagramming looks like in its interactive form. Please note + that this demo of the interface is not compatible with mobile devices ...
In this conclusion, we reflect upon a final dimension of the method + of diagraming in the context of figurations and configurations: its + potential as an alternative to the conventional research program.
+While writing this text, indeed, the search for a coherent structure + through which we could map the problems that emerged from analyzing the + diagrams in a straightforward narrative proved elusive. We considered + various organizational frameworks, but consistently encountered + resistance from one or two sections. It became evident that our + interviews yielded a rhizome of interrelated problems, creating a + multitude of possible inquiries and overlapping trajectories. Some + dimensions of these problems are related, but not to every problem.
+If we take for example the understanding of algorithmic security + vision as practices of error management as a starting point, we see how + the actors we interviewed have incorporated the societal critique of + algorithmic bias. This serves as a catalyst for novel strategies aimed + at mitigating the repercussions of imperfect systems. The societal + critique has driven the development of synthetic datasets, which promise + equitable representation across diverse demographic groups. It has also + been the reason for the reliance on institutionalized benchmarks to + assess the impartiality of algorithms. Moreover, different + configurations of the human-in-the-loop emerge, all promised to rectify + algorithmic fallibility. We see a causal chain there.
+But how does the question of algorithmic error relate to the shift + from photographic to cinematic vision that algorithmic security vision + brings about? Certainly, there are reverberations. The relegation of + stable identity that we outlined, could be seen as a way to mitigate the + impact of those errors. But it would be a leap to identify these + questions of error as the central driver for the increased incorporation + of moving images in algorithmic security vision.
+However, if we take as our starting point the formidable strides in + computing power and the advancements in camera technologies, we face + similar problems. These developments make the analysis of movement + possible while helping to elucidate the advances in the real-time + analysis that are required to remove the human-in-the-loop, as trialed + in the Burglary-Free Neighborhood. These developments account for the + feasibility of the synthetic data generation, a computing-intense + process which opens a vast horizon of possibilities for developers to + detect objects or actions. Such an account, however, does not address + the need for such a synthetic dataset. A focus on the computation of + movement, however, would highlight how a lack of training data + necessitates many of the practices described. Synthetic data is + necessitated by the glaring absence of pre-existing security datasets + that contain moving bodies. While facial recognition algorithms could be + trained and operated on quickly repurposed photographic datasets of + national identity cards or drivers’ license registries, no dataset for + moving bodies has been available to be repurposed by states or + corporations. This absence of training data requires programmers to + stage scenes for the camera. Thus, while one issue contains echoes of + the other, the network of interrelated problematizations cannot be + flattened into a single narrative.
+The constraints imposed by the linear structure of an academic + article certainly necessitate a specific ordering of sections. Yet the + different research directions we highlight form something else. The + multiple figurations analyzed here generate fresh tensions when put in + relation with security and political practices. What appears from the + diagrams is a network of figurations in various configurations. Instead + of a research program, our interviews point toward a larger + research diagram of interrelated questions, which invites us to + think in terms of pathways through this dynamic and evolving network of + relations.
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+The interviews + were conducted in several European countries: the majority in the + Netherlands, but also in Belgium, Hungary and Poland. Based on an + initial survey of algorithmic security vision practices in Europe we + identified various roles that are involved in such practices. Being a + rather small group of people, these interviewees do not serve as + “illustrative representatives” (Mol & Law 2002, 16-17) of the field + they work in. However, as the interviewees have different cultural and + institutional affiliations, and hold different positions in working with + algorithms, vision and security, they cover a wide spectrum of + engagements with our research object.
+The interviews + were conducted by the first two authors, and at a later stage by Clemens + Baier. The conversations were largely unstructured, but began with two + basic questions. First, we asked the interviewees if they use diagrams + in their daily practice. We then asked: “when we speak of ‘security + vision’ we speak of the use of computer vision in a security context. + Can you explain from your perspective what these concepts mean and how + they come together?” After the first few interviews, we identified some + recurrent themes, which we then specifically asked later interviewees to + discuss.
+Using + anthropomorphizing terms such as “neural networks,” “learning” and + “training” to denote algorithmic configurations and processes is + suggested to hype “artificial intelligence.” While we support the need + for an alternative terminology as proposed by Hunger (2023), here we + preserve the language of our interviewees.
+