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1717 lines
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<meta name="author" content="Ruben van de Ven, Ildikó Zonga Plájás, Cyan Bae, Francesco Ragazzi" />
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<title>Algorithmic Security Vision: Diagrams of Computer Vision Politics</title>
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<header id="title-block-header">
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<h1 class="title">Algorithmic Security Vision: Diagrams of Computer
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Vision Politics</h1>
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<p class="author"><em>Ruben van de Ven, Ildikó Zonga Plájás, Cyan Bae,
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Francesco Ragazzi</em></p>
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<p class="date">December 2023</p>
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</header>
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<section id="abstract" class="level1">
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<h1>Abstract</h1>
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<div data-custom-style="Body Text">
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<p>More images than ever are being processed by machine learning
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algorithms for security purposes. Yet what technical and political
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transformations do these sociotechnical developments create? This paper
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charts the development of a novel set of practices which we term
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"algorithmic security vision" using a method of diagramming-interviews.
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Based on descriptions by activists, computer scientists and security
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professionals, this article marks three shifts in security politics: the
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emergence of synthetic data; the increased importance of movement,
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creating a cinematic vision; and the centrality of error in the design
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and functioning of these systems. The article then examines two tensions
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resulting from these shifts: a fragmentation of accountability through
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the use of institutionalized benchmarks, and a displacement of
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responsibility through the reconfiguration of the human-in-the-loop. The
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study of algorithmic security vision thus engenders a rhizome of
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interrelated configurations. As a diagram of research, algorithmic
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security vision invites security studies to go beyond a singular
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understanding of algorithmic politics, and think instead in terms of
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trajectories and pathways through situated algorithmic practices.</p>
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</div>
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</section>
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<section id="introduction" class="level1">
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<h1>Introduction</h1>
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<div data-custom-style="Body Text">
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<p>In cities and at borders around the world, algorithms process streams
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of images produced by surveillance cameras. For decades, <em>computer
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vision</em> has been used to analyze security imagery using arithmetic
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to, for example, send an alert when movement is detected in the frame,
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or when a perimeter is breached. The increases in computing power and
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advances in (deep) machine learning have reshaped the capabilities of
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such security devices. These devices no longer simply quantify vast
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amounts of image sensor data but qualify it to produce interpretations
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in previously inconceivable ways. Pilot projects and off-the-shelf
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products are intended to distinguish individuals in a crowd, extract
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information from hours of video footage, gauge emotional states,
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identify potential weapons, discern normal from anomalous behavior, and
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predict intentions that may pose a security threat. Security practices
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are substantially reconfigured through the use of machine learning-based
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computer vision, or "algorithmic security vision."</p>
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</div>
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<div data-custom-style="Body Text">
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<p>Algorithmic security vision represents a convergence of security
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practices and what Rebecca Uliasz calls <em>algorithmic vision</em>: the
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processing of images using machine learning techniques to produce a kind
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of "vision" that does not make sense of the “visual” but that makes
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realities actionable (Uliasz, 2020). It does not promise to eradicate
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human sense making, but rather allows a reconsideration of how human and
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nonhuman perception is interwoven with sociotechnical routines.
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Algorithmic security vision thus draws together actors, institutions,
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technologies, infrastructures, legislations, and sociotechnical
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imaginaries (see Bucher, 2018: 3). Yet how does algorithmic security
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vision work — <em>how</em> does it draw together these entities— and
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what are the social and political implications of its use? In this
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article we explain how “algorithmic vision” and “security” can map out
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sociotechnical practices and explore how their coming together reframes
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what it means to see and suspect. We are not concerned with the
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technical features of the systems, but with the societal and political
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projects that are embedded in technical choices made in their
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construction.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>We ground this article in Lucy Suchman’s notions of
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<em>figuration</em> and <em>configuration</em> as both a conceptual
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frame and a method of analysis which offer insight into the interplay of
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technology, imaginaries, and politics. <em>Configuration</em> allows
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access to key assumptions about the boundaries that are negotiated in
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the practices of algorithmic security vision, and how entities solidify
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and stabilize as they circulate. The realities that are made possible by
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algorithms are performed and perpetuated in the design and description
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of these systems (Suchman, 2006: 239; see also Barad, 2007: 91).</p>
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</div>
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<div data-custom-style="Body Text">
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<p>To grasp the specificities of algorithmic security vision, we turn to
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the professionals who work with those technologies. How do people
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working with algorithmic security vision make sense of, or
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<em>figure</em>, their practices? An important dimension of this paper
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is therefore methodological. Suchman, drawing on Haraway, mobilizes the
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trope of the <em>figure</em> to examine the construction and circulation
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of concepts: "to figure is to assign shape, designate what is to be made
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noticeable and consequential, to be taken as identifying” (Suchman,
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2012: 49). To expand on traditional textual analysis of such figurations
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we introduce time-based diagramming where we combine qualitative
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interviews with drawing. With these diagrams that record both voice and
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the temporal unfolding of the drawing, the figurations appear in spatial
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and temporal dimensions.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>We begin by situating our research in the debates on sensors,
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algorithms and power, and outlining our theoretical and methodological
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approach. Then, drawing on the time-based diagrams, we discuss three
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figurations that challenge us to rethink our understandings of
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algorithmic vision in security: algorithmic vision as synthetically
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trained and cinematic, and the error as an inherent feature of
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algorithmic vision. In a second step, we outline the fragmentation of
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accountability through the use of benchmarks, and the reconfigurations
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of the human-in-the-loop.</p>
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</div>
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</section>
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<section id="sensors-algorithms-power" class="level1">
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<h1>Sensors, Algorithms, Power</h1>
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<div data-custom-style="Body Text">
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<p>The critical reflection on the politics of algorithmic security
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systems is not novel in Geography or in interdisciplinary debates, along
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with Science and Technology Studies, Critical Security Studies or Media
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Studies (Fourcade and Gordon, 2020; Graham, 1998; Mahony, 2021; Schurr
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et al., 2023). Yet aside from a few exceptions (Andersen, 2018;
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Bellanova et al., 2021), the politics specific to computer vision in the
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security field have been overlooked.</p>
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</div>
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<div data-custom-style="Body Text">
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<p><em>Computer vision</em> is a term used to designate a multiplicity
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of algorithms that can process still or moving images, producing
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information upon which human or automated systems can make decisions. It
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is meant to replicate certain aspects of human cognition. Algorithms can
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be used to segment parts of an image, detect and recognize objects or
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faces, track people or objects, estimate motion in a video, or
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reconstruct 3D models based on multiple photo or video perspectives
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(Dawson-Howe, 2014).</p>
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</div>
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<div data-custom-style="Body Text">
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<p>Some scholars working on algorithmic security have addressed the role
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of "operative images," which are "images that do not represent and
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object, but rather are part of an operation" (Farocki, 2004). Authors
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||
have shown how algorithms organize the regimes of visibility in
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platforms such as YouTube and Facebook (Andersen, 2015), in war and
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especially in military drone strikes (Bousquet, 2018; Suchman, 2020;
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Wilcox, 2017). Others have focused on machine-mediated vision at the
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European border by analyzing the functioning of EUROSUR (Dijstelbloem et
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||
al., 2017; Tazzioli, 2018;) and SIVE (Fisher, 2018).</p>
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||
</div>
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<div data-custom-style="Body Text">
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||
<p>These studies have contributed to theoretical debates around novel
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||
practices algorithmic power (Bucher, 2018), surveillance capitalism
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(Srnicek and De Sutter, 2017; Zuboff, 2019) and platform politics
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(Carraro, 2021; Gillespie, 2018). Some works have described the social
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and political effects of surveillance and social sorting (Gandy, 2021;
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Lyon, 2003), as well as the reinforcement of control and marginalization
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of post-colonial, gendered and racialized communities (Fraser, 2019;
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Thatcher et al., 2016), defined by Graham as "software-sorted
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Geographies" (Graham, 2005).</p>
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</div>
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<div data-custom-style="Body Text">
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<p>These debates have highlighted the entanglement of these technologies
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with risk assessment and pre-emptive security logics (Amoore, 2014;
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Aradau and Blanke, 2018). Critical work has started catching up with
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machine learning as an algorithmic technique (Amoore, 2021; Mackenzie,
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2017), marking a shift from the management of "populations" to
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"clusters," the acceleration of knowledge feedback loops (Isin and
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Ruppert, 2020), foregrounding the normalization of behavior through the
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regulation of the "normal" and the "anomaly" (Aradau and Blanke, 2018).
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Or, in Pasquinelli’s words, how algorithms "normalize the abnormal
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<em>in a mathematical way</em>" (Pasquinelli, 2015: 8 emphasis in
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original).</p>
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</div>
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<div data-custom-style="Body Text">
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<p>Yet what characterizes the state of the literature is a segmentation
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between work on the politics of the “sensor,” and those on the political
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specificities of deep learning models.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>On the one hand, using the notion of “sensor society,” Mark
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Andrejevic and Mark Burdon (2015) have noted the prevalence of embedded
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and distributed sensors. They have noted a shift from targeted,
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purposeful, and discrete forms of information collection to always-on,
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ubiquitous, opportunistic ever-expanding forms of data capture.
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Andrejevic and Burdon insist that the sensors are only part of the
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story; infrastructures are also critical: “It is […] the potential of
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the automated processing of sensor-derived data that underwrites the
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productive promise of data analytics in the sensor society: that the
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machines can keep up with the huge volumes of information captured by a
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distributed array of sensing devices” (Andrejevic and Burdon, 2015: 27).
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Yet their focus is more on the sensors than on the underlying
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algorithmic infrastructures.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>In their work on “sensory power,” Engin Isin and Evelyn Ruppert have
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recently analyzed the effect of recent developments in technological
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software and infrastructure. Unlike the three traditional forms of power
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identified by Foucault (sovereign, disciplinary, and regulatory) they
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argue that sensory power operates through apps, devices, and platforms
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||
to collect and analyze data about individuals' bodies, behaviors, and
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environments. For Isin and Ruppert, the central notion of sensory power
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is the cluster. Clusters do not merely constitute "new" representations
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||
of "old" populations, but rather “intermediary objects of government
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||
between bodies and populations that a new form of power enacts and
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governs through sensory assemblages” (Isin and Ruppert, 2020: 7).
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Despite their contribution to, thinking about sorting techniques and
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their relations to new forms of power Isin and Ruppert, like Andrejevic
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and Burdon, bracket the specificities of the underlying deep learning
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models.</p>
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</div>
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<div data-custom-style="Body Text">
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<p>A growing body of literature has explored the politics of machine
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learning techniques. In her latest work on the “deep border,” Louise
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Amoore revisits her 2006 essay on the “biometric border.” Her focus is
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on “deep machine learning,” and the “capacity to abstract and to
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represent the relationships in high-dimensional data” such as in image
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recognition (Amoore, 2021: 6). She shows that the change in border
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technologies, from simple IF-THEN algorithmics with pre-determined
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variables, to complex, deep, “neural networks” characterized by the
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indeterminacy of variables marked a profound change in the logic, and
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thus the political effects of these technologies. Like Isin and Ruppert
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she is interested in the notion of the “cluster,” which, “with its
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attendant logic of iterative partitioning and rebordering, loosens the
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state’s application of categories and criteria in borders and
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immigration” (Amoore, 2021: 6). Yet her approach overlooks the
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||
importance of sensorial data posited by Andrejevic and Burdon, and Isin
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and Ruppert.</p>
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||
</div>
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<div data-custom-style="Body Text">
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<p>In sum, we still have only a rudimentary understanding of the
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politics of algorithmic security vision. So, how does one think
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||
politically about the new relations among sensors, algorithmic vision,
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and politics? We propose a methodology for exploratory research that can
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help outline a research agenda.</p>
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</div>
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||
</section>
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<section id="methodology" class="level1">
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<h1>Methodology</h1>
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<section id="configuration-as-a-methodological-device" class="level2">
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<h2>Configuration as a methodological device</h2>
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<div data-custom-style="Body Text">
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<p>Recent scholarship on technology and security has emphasized the
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importance of algorithmic systems as enacted through relations between
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human and nonhuman actors (Aradau and Blanke, 2015; Bellanova et al.,
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2021; Hoijtink and Leese, 2019; Suchman, 2006). Sociotechnical systems
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act in "co-production" (Jasanoff, 2004), as "actants" in a network
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(Latour, 2005), or in "intra-action" (Barad, 2007). In these
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understandings, technology forms an ontological assemblage, in which
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human agency is tied in with the sociomaterial arrangements of which it
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is part. Humans and non-humans, technological objects and
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infrastructures, all populate complex, sometimes messy networks where
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the boundaries between entities are enacted in situated practices
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(Haraway, 1988). This conception of technology "draws attention to the
|
||
fact that these relations are not a given but that they are constructed
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||
— and thereby relates them back to cultural imaginaries of what
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technology should look like and how it should be positioned vis-à-vis
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humans and society" (Leese, 2019: 45)</p>
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||
</div>
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<div data-custom-style="Body Text">
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<p>In this context, how can we understand the characteristics and
|
||
effects of security systems built on the analysis of sensor data through
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||
“deep learning,” and the new security politics that they introduce? On
|
||
the technical level, the novelty of “algorithmic security vision” does
|
||
not lie in the sensors themselves, but in the new abilities of
|
||
“artificial intelligence software” (McCosker and Wilken, 2020). The
|
||
promise of the systems is that the multiplication of the sensors and
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||
modalities of knowing, the ability to create information feeds that are
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||
under the scrutiny of automated systems means that data collection and
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||
data analysis are no longer separated; surveillance can happen in real
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||
time, capturing life as it unfolds, so that the operators can act on
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||
hotspots, clusters, or the moods and emotions of a crowd (Andrejevic and
|
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Burdon, 2015; Isin and Ruppert, 2020).</p>
|
||
</div>
|
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<div data-custom-style="Body Text">
|
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<p>To make sense of such developments, Suchman’s concept of
|
||
configuration is a useful methodological “toolkit.” It helps
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||
“delineating the composition and bounds of an object of analysis”
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||
(Suchman, 2012: 48) and allows us to conceptualize algorithmic security
|
||
vision as heterogeneous assemblages of human and nonhuman elements whose
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||
agency is "an effect of practices that are multiply distributed and
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||
contingently enacted" (Suchman, 2006: 267). We are interested here in a
|
||
framework that underscores "how the entities that come into relation are
|
||
not given in advance, but rather emerge through the encounter with one
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||
another" (van de Ven and Plájás, 2022: 52).</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Suchman also draws our attention to the “ways in which technologies
|
||
materialize cultural imaginaries, just as imaginaries narrate the
|
||
significance of technical artefacts” (2012: 48). For Suchman,
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||
“configuration” is a tool for “studying technologies with particular
|
||
attention to the imaginaries and materialities that they join together”
|
||
(2012: 48). The configuration of humans and machines is constructed
|
||
through discourse and practice, which, drawing on Haraway, she
|
||
conceptualizes as “figurations.” Sociotechnical systems thus do not
|
||
exist without their intended uses and users. Such discourses are an
|
||
important part of individual experience, collective professional
|
||
practices, and narratives about technology. Technologies bring together
|
||
elements from various registers into stable material-semiotic
|
||
arrangements. Those configurations draw attention to the political
|
||
effects of everyday practices and how they institute bounded entities
|
||
and their relations. If we take Suchman’s suggestion that algorithmic
|
||
security vision is complex and multiple, how can we get to "know" it as
|
||
an object of research, while acknowledging its partiality? When taking
|
||
the coming together of algorithms, vision and (in)security as
|
||
configuring imaginaries and practices in heterogeneous and complex
|
||
networks, how can we explore their politics?</p>
|
||
</div>
|
||
</section>
|
||
<section id="time-based-diagramming" class="level2">
|
||
<h2>Time-based Diagramming</h2>
|
||
<div data-custom-style="Body Text">
|
||
<p>Suchman defines <em>figuration</em> as “action that holds the
|
||
material and the semiotic together in ways that become naturalized over
|
||
time, and in turn requires ‘unpacking’ to recover its constituent
|
||
elements” (2012: 49). The first step in her methodology therefore
|
||
requires us to “reanimate the figure at the heart of a given
|
||
configuration, in order to recover the practices through which it comes
|
||
into being and sustains its effects.”</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>In her work, Suchman has used a variety of methods of inquiry to
|
||
“reanimate the figure.” Qualitative interviews and ethnography have been
|
||
instrumental in producing the raw material for the analysis. In this
|
||
paper, we expand the methodological toolkit envisaged by Suchman to
|
||
multimodal methods that go beyond text to capture the materiality of
|
||
imaginaries and practices. We explore the epistemic possibilities of
|
||
capturing figurations as both semiotic and material traces.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>The result of our theoretical and methodological quest is a tool that
|
||
allows us to produce “time-based diagramming.” We use this method for
|
||
both elicitation and multimodal data collection. We presented our
|
||
participants with a large digital tablet, and asked them to draw a
|
||
diagram while answering our questions. Ruben van de Ven programmed an
|
||
interface that could play back the recorded conversation in drawing and
|
||
audio. The participants could not delete or change their drawings, so
|
||
their hesitations and corrections remained. The ad hoc <em>figuring
|
||
out</em> of the participants’ descriptions thus remains part of the
|
||
recording.<a href="#fn1" class="footnote-ref" id="fnref1"
|
||
role="doc-noteref"><sup>1</sup></a> In the phase of data analysis, the
|
||
software allows the diagrams to be annotated, creating short clips. The
|
||
diagrams thus enable a practice of combination and composition
|
||
(O’Sullivan, 2016), providing for a material-semiotic support to analyze
|
||
various imaginaries of algorithmic security vision.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Diagramming is a key method in the field of technology, most notably
|
||
in the conceptualization and design of computational practices
|
||
(Mackenzie, 2017; Soon and Cox, 2021: 221). We don’t assume that the
|
||
materiality of the drawings brings us any closer to the materiality of
|
||
the actors’ practices, which are of a different order. Our interest is
|
||
in the possibilities offered by the diagrams: they are composed of
|
||
elements that are not necessarily similar, but are connected by their
|
||
mere appearance on the same plane, thus allowing heterogeneous elements
|
||
to co-exist. Diagrams are composed of parts that can be separated and
|
||
recombined in different ways, creating new formations and expressions
|
||
(O’Sullivan, 2016). Using such a multimodal tool seemed a pertinent
|
||
methodological setup to capture <em>figurations</em> and
|
||
<em>configurations</em> (see van de Ven and Plájás, 2022)<em>.</em></p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>We interviewed twelve professionals who developed, deployed or
|
||
contested computer vision technologies in the field of (in)security.<a
|
||
href="#fn2" class="footnote-ref" id="fnref2"
|
||
role="doc-noteref"><sup>2</sup></a> We asked them to describe the coming
|
||
together of computer, vision and (in)security from their professional
|
||
vantage points.<a href="#fn3" class="footnote-ref" id="fnref3"
|
||
role="doc-noteref"><sup>3</sup></a> In what follows, we focus on three
|
||
figurations and two configurations that emerged from the diagrams.</p>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<section id="figurations-of-algorithmic-security-vision" class="level1">
|
||
<h1>Figurations of algorithmic security vision</h1>
|
||
<div data-custom-style="Figure">
|
||
<p><img src="assets//media/image1.png"
|
||
style="width:1.67431in;height:1.11736in" /><img
|
||
src="assets//media/image2.png"
|
||
style="width:2.04861in;height:1.11736in" /><img
|
||
src="assets//media/image3.png"
|
||
style="width:2.27986in;height:1.08056in" /></p>
|
||
</div>
|
||
<div data-custom-style="Caption">
|
||
<p>Diagram 1. Collage of excerpts from the conversations. Computer
|
||
vision is often depicted as camera based. The third drawing depicts a
|
||
"sensor hotel" on top of a light post in the Burglary-Free
|
||
Neighborhood.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>To understand the politics involved in the introduction of
|
||
algorithmic vision in security practices, the first step was to see how
|
||
the practitioners we spoke with <em>figured</em> their own practices
|
||
through the use of our diagramming method. Our aim was to capture
|
||
through shapes, relations, associations, and descriptions, the actors,
|
||
institutions, technical artifacts, and processes in situated practices
|
||
of algorithmic security vision.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>When we asked our interviewees what unites practices of computer
|
||
vision in (in)security, they start by foregrounding the camera and the
|
||
(algorithmically processed) visual image. However, when they began
|
||
drawing these assemblages based on examples, complexities emerged. In an
|
||
example of crowd detection developed for the securitization of the
|
||
Hague’s seaside boulevard, multiple sensors are installed on lampposts
|
||
and benches to count passersby. Based on behaviors and moving patterns
|
||
in the public space, operators can know, how many people are on the
|
||
boulevard at a certain moment, and whether these are individuals, or
|
||
small or large groups — the latter of which might be seen as a potential
|
||
security threat. The Burglary-Free Neighborhood in Rotterdam uses a
|
||
“sensor hotel” installed under the hood of street lamps (Diagram 1)
|
||
where the trajectory of pedestrians is analyzed with sounds like
|
||
breaking glass, gunshots or screams. In the security assemblages
|
||
described by our interviewees, the camera is but one element. During the
|
||
diagramming, the figure of the visual is pushed out of focus.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>In analyzing the twelve diagrams, three central figurations in
|
||
camera-based algorithmic security practices emerged that help us to
|
||
rethink some central notions of the literature on algorithmic security:
|
||
(1) a figure of “vision” as increasingly trained synthetically, not
|
||
organically; (2) a figure of vision as cinematic and moving in time, not
|
||
photographic; (3) a figure of the error as a permanent dimension of
|
||
algorithmic vision, not as something that could be solved or
|
||
eliminated.</p>
|
||
</div>
|
||
<section id="from-skilled-vision-to-synthetic-vision" class="level2">
|
||
<h2>1. From skilled vision to synthetic vision</h2>
|
||
<div data-custom-style="Figure">
|
||
<p><img src="assets//media/image4.png"
|
||
style="width:3.38681in;height:2.57708in" /></p>
|
||
</div>
|
||
<div data-custom-style="Caption">
|
||
<p>Diagram 2. Sergei Miliaev distinguishes three sources of training
|
||
data for facial recognition technologies.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>In most of our conversations, algorithmic security vision is
|
||
understood to involve a particular subset of algorithms: deep neural
|
||
networks.<a href="#fn4" class="footnote-ref" id="fnref4"
|
||
role="doc-noteref"><sup>4</sup></a> Such a machine learning-based vision
|
||
brings to the fore one key dimension of security practices: the question
|
||
of training, and the ability to “see.” Training has been assumed as part
|
||
of the discussion around the socialization of security professionals
|
||
(Amicelle et al., 2015; Bigo, 2002), and algorithmic systems (Fourcade
|
||
and Johns, 2020) but scant attention has been paid to how training
|
||
elaborates upon and incorporates specific sets of skills.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Some authors have explored the way in which the “seeing” of security
|
||
agents is trained at the border, by building on the literatures on
|
||
“skilled vision” (Maguire et al., 2014) or “vision work” (Olwig et al.,
|
||
2019). Maguire mobilized work in anthropology that locates human vision
|
||
as a “embodied, skilled, trained sense” (Grasseni, 2004: 41) that
|
||
informs standardized practices of local “communities of vision” (see
|
||
also Goodwin, 1994). Skilled vision is useful in that it draws attention
|
||
to the sociomaterial circumstances under which vision becomes a trained
|
||
perception (Grasseni, 2018: 2), and how it becomes uniform in
|
||
communities through visual apprenticeship. This literature examines the
|
||
production of “common sense” by taking training, exercise, peer
|
||
monitoring and other practices of visual apprenticeship as locus of
|
||
attention. Yet these works fail to capture the specificities of the type
|
||
of machine learning we encountered in our research. How then is visual
|
||
apprenticeship reconfigured under algorithmic security vision?</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>In our conversations, the “training of the algorithms,” figures as a
|
||
key stake of algorithmic security vision. The participants in our
|
||
diagram interviews explained how deep learning algorithms are trained on
|
||
a multiplicity of visual data which provides the patterns a system
|
||
should discriminate on. In Diagram 2, Sergei Miliaev, head of the facial
|
||
recognition research team at VisionLabs in Rotterdam illustrated this
|
||
point.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Miliaev distinguishes three sources for training images: web
|
||
scraping, “operational” data collected through its partners or clients,
|
||
and “synthetic” data. The first two options, Miliaev argues, have some
|
||
limitations. Under European data protection regulation it is very
|
||
difficult to obtain or be allowed to use data “from the wild” because it
|
||
is often illegal to collect data of real people in the places where the
|
||
algorithm will be used. Additionally, partners sometimes resist sharing
|
||
their operational footage outside of their own digital infrastructures.
|
||
Finally, when engineering a dataset, one cannot control what kind of
|
||
footage is encountered in “the wild.” This has led to the emergence of a
|
||
new phenomenon: training data generated in the lab.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Synthetic training data is often collected by acting in front of a
|
||
camera. We see this in the case of intelligent video surveillance
|
||
(<em>Intelligente</em> <em>Videoüberwachung</em>) deployed in Mannheim
|
||
since 2018. Commenting on this case, chief of police
|
||
(<em>Polizeidirektor)</em> Dirk Herzbach explains that self-defense
|
||
trainers imitated 120 body positions to create the annotated data used
|
||
to train the behavior recognition technology. In another example, Gerwin
|
||
van der Lugt, developer of software that detects violent behavior,
|
||
stated that given the insufficiency of data available, they “rely on
|
||
some data synth techniques,” such as simulating violent acts in front a
|
||
green screen. Sometimes even the developers, computer scientists or
|
||
engineers themselves re-enact certain movements or scenes for training
|
||
their algorithms. In Diagram 3, two developers involved in the project
|
||
at seaside boulevard in Scheveningen give a striking example of how such
|
||
enactments of suspicious events require the upfront development of a
|
||
threat model that contains visual indicators that distinguish threat (a
|
||
positive detection) from non-threat (a negative detection). The acting
|
||
of the developers embeds these desirable and undesirable traits into the
|
||
computer model.</p>
|
||
</div>
|
||
<div data-custom-style="Figure">
|
||
<p><img src="assets//media/image5.png"
|
||
style="width:3.95in;height:3.075in" /></p>
|
||
</div>
|
||
<div data-custom-style="Caption">
|
||
<p>Diagram 3. Two developers involved in a project at the seaside
|
||
boulevard in Scheveningen describe the use of computer vision to
|
||
distinguish the legal use balloons from their illegal use for inhaling
|
||
the nitrous oxide gas.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Sometimes the meaning of “synthetic” or “fake” data is pushed. Sergei
|
||
Miliaev explains how in the context of highly sensitive facial
|
||
recognition algorithms, software companies use faces generated entirely
|
||
through artificial neural networks to train their algorithms. Miliaev
|
||
mentions Microsoft’s DigiFace-1M (Bae et al., 2023), a training dataset
|
||
containing one million algorithmically generated faces. Such synthetic
|
||
training sets complicate the borders between sensor-originated and other
|
||
types of images. In the use of artificially generated images, one GPU
|
||
generates bytes that are interpreted by another. Algorithmic vision
|
||
occurs without direct reference to people or things that live outside
|
||
electronic circuits.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>These technical developments offer a changing figure of the skilled
|
||
vision of security that calls for new research directions. While these
|
||
technologies are still in their infancy, our interviewees see it as a
|
||
token of “better” and “fairer” technology that can circumvent racial
|
||
bias, as any minority can be generated to form an equal distribution in
|
||
the training dataset (Stevens and Keyes, 2021). But with an emerging
|
||
concern for algorithmic hallucination (Ji et al., 2023), glitches or
|
||
undesirable artifacts in the generated data, one wonders what kind of
|
||
vision is trained using such collections. Learning from synthetic data
|
||
thus produces an internalized vision, providing insights by circulating
|
||
data through a chain of artificial neural networks. While appearing in
|
||
new technological assemblages, the processing of images to form
|
||
archetypes is reminiscent of the composite photographs created by Galton
|
||
(1879). His composites were used to train police officers to identify
|
||
people as belonging to a particular group, circulating and reinforcing
|
||
the group boundaries based on appearance (Hopman and M’charek, 2020).
|
||
Which boundaries does “fake” or synthetized training data perpetuate?
|
||
Skilled vision shifts attention to the negotiations that happen before
|
||
algorithmic vision is trained, such as how algorithmic vision depends on
|
||
access to data and regulations around data protection.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>With the use of synthetic data, the question of a "community of
|
||
lookers" — the embodied social and material practices through which
|
||
apprenticeship is perpetuated — appears in a new light. Such a community
|
||
becomes more dispersed as generative models circulate freely online. For
|
||
instance, a generative model from Microsoft, trained on images shared
|
||
online, is used for training an authentication system in the Moscow
|
||
Metro system. Such models are informed by communities of looking from
|
||
which their training data is sourced, and the norms of that platform.
|
||
These norms then circulate with the model and become "plugged-in" to
|
||
other systems. Algorithmic vision, trained on synthetic data, is thus a
|
||
composable vision, in which different sources of training data mobilize
|
||
imagery from all kinds of aesthetic apprenticeships. The cascading of
|
||
generative and discriminative models thus reshapes security practices.
|
||
Furthermore, to comprehend changes in the politics of vision, attention
|
||
to the training of vision, as a moment of standardization and
|
||
operationalization, could be extended to the training of security
|
||
professionals.</p>
|
||
</div>
|
||
</section>
|
||
<section id="figuring-time-from-photographic-to-cinematic-vision"
|
||
class="level2">
|
||
<h2><span data-custom-style="Heading 3 Char">2. Figuring time: from
|
||
photographic to cinematic vision</span></h2>
|
||
<div data-custom-style="Body Text">
|
||
<p>Conversations with practitioners revealed yet another dimension of
|
||
the figure of vision in flux: its relation to time and movement. Deep
|
||
learning-based technologies distinguish themselves from earlier
|
||
algorithmic security systems based on their status as prediction models,
|
||
which by definition raises questions on the temporal dimensions of their
|
||
processing (Sudmann, 2021). Yet, how algorithmic security vision
|
||
reconfigures temporalities has yet to receive scholarly attention in CSS
|
||
and related disciplines. While literature on border studies has located
|
||
border security in multiple places and temporalities (e.g. Bigo and
|
||
Guild, 2005), scholarship on image-based algorithmic security practices
|
||
have often focused on a photography-centric paradigm: biometric images
|
||
(Pugliese, 2010) facial, iris and fingerprint recognition (Møhl, 2021),
|
||
and body scanners (Leese, 2015). These technologies capture immutable
|
||
features of suspect identities. In the diagrams, however, vision appears
|
||
less static. Instead, two central dimensions of the figure of vision
|
||
appear: the ability to capture and make sense of the movement of the
|
||
bodies in a fixed space, and the movement of bodies across spaces.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>On the first point, we notice increasing attention to corporeality,
|
||
how physical movements render certain individuals suspicious. This
|
||
process takes place through the production and analysis of motion by
|
||
composing a sequence of frames. Gerwin van der Lugt, who helped develop
|
||
a violence detection algorithm at Oddity.ai, stresses how “temporal
|
||
information integration” is the biggest technical challenge in detecting
|
||
violence in surveillance footage: a raised hand might be either a punch
|
||
or a high-five. In Diagram 4, van der Lugt visualizes the differences
|
||
between the static and dynamic models. A first layer of pose or object
|
||
detection often analyzes a merely static image. Oddity.ai then uses
|
||
custom algorithms to integrate individual detections into one that
|
||
tracks movement. It is then the movement that can be assessed as violent
|
||
or harmless. From these outputs, Oddity.ai runs “another [...] process
|
||
that [they] call temporal information integration—it’s quite
|
||
important—to [...] find patterns that are [even] longer.” This case
|
||
illustrates how algorithmic security vision temporarily attributes risk
|
||
to bodies, in accordance with the ways violence is imagined and
|
||
choreographed in the training data.</p>
|
||
</div>
|
||
<div data-custom-style="Figure">
|
||
<p><img src="assets//media/image6.png"
|
||
style="width:5.80694in;height:4.61944in" /></p>
|
||
</div>
|
||
<div data-custom-style="Caption">
|
||
<p>Diagram 4. Top: Frame 1 is processed by YOLO, an object detection
|
||
model, producing Output 1 (O1). Other frames are processed
|
||
independently. Bottom: Frames 1 to 10 are combined for processing by the
|
||
customized model (“M”), where it produces outputs (O 1-10, O 11-20).
|
||
These outputs are then processed in relation to one another by the
|
||
temporal information integration to find body patterns over longer
|
||
periods. Drawn by Gerwin van der Lugt.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Our interviewees figured movement in a second way. Bodies are tracked
|
||
in space, leading to an accumulation of suspicious data over time. Ádám
|
||
Remport explained how facial recognition technology (FRT) works by
|
||
drawing a geographical map featuring building blocks and streets. In
|
||
this map (Diagram 5), Person A could visit a bar, a church, or an
|
||
NGO.</p>
|
||
</div>
|
||
<div data-custom-style="Figure">
|
||
<p><img src="assets//media/image7.png"
|
||
style="width:5.13194in;height:2.37014in" /></p>
|
||
</div>
|
||
<div data-custom-style="Caption">
|
||
<p>Diagram 5. Ádám Remport explains how a person’s everyday routes can
|
||
be inferred when facial recognition technology is deployed in various
|
||
sites, montaging a local, photographic vision into spatio-temporal,
|
||
cinematic terms.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>If “FRT is fully deployed and constantly functioning,” explains
|
||
Remport, people can be “followed wherever [they] go.” Remport’s drawing
|
||
therefore suggests that in this setting it is not important to be able
|
||
to identify the person under surveillance; what matters is that this
|
||
person can be tracked over different surveillance camera feeds. The
|
||
trajectories of bodies and their “signature” marked through the
|
||
reconstruction of their habitual movements through space are used as a
|
||
benchmark for the construction of suspicion. Cinematic vision is thus
|
||
made possible thanks to the broader infrastructure that allows for the
|
||
collection and analysis of data over longer periods of time, and their
|
||
summarization through montage.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>The emerging centrality of movement thus opens up a new research
|
||
agenda for security, focused not on <em>who</em> and what features are
|
||
considered risky, but <em>when</em>, and through which movements
|
||
specific bodies become suspicious. While earlier studies on biometric
|
||
technologies have located the operational logic on identification,
|
||
verification, authentication, thus <em>knowing</em> the individual
|
||
(Ajana, 2013; Muller, 2010), figuring algorithmic security vision as
|
||
cinematic locates its operational logic in the mobility of embodied life
|
||
(see Huysmans, 2022). While many legal and political debates revolve
|
||
around the storage of images as individual frames, and the privacy
|
||
issues involved, less is known about the consequences of putting these
|
||
frames into a sequence on a timeline and the movements that emerge
|
||
through the integration of frames over time.</p>
|
||
</div>
|
||
</section>
|
||
<section id="managing-error-from-the-sublime-to-the-risky-algorithm"
|
||
class="level2">
|
||
<h2>3. Managing error: from the sublime to the risky algorithm</h2>
|
||
<div data-custom-style="Body Text">
|
||
<p>Our third emerging figuration concerns the place of the error. A
|
||
large body of literature examines actual and speculative cases of
|
||
algorithmic prediction based on self-learning systems (Azar et al.,
|
||
2021). Central to these analyses is the boundary-drawing performed by
|
||
such algorithmic devices, enacting (in)security by rendering their
|
||
subjects as more- or less-risky others (Amicelle et al., 2015: 300;
|
||
Amoore and De Goede, 2005; Aradau et al., 2008; Aradau and Blanke, 2018)
|
||
based on a spectrum of individual and environmental features (Calhoun,
|
||
2023). In other words, these predictive devices conceptualize risk as
|
||
something produced by, and thus external to, security technologies.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>In this critical literature on algorithmic practices, practitioners
|
||
working with algorithmic technologies are often critiqued for
|
||
understanding software as “sublime” (e.g. Wilcox, 2017: 3). However, in
|
||
our diagrams, algorithmic vision appears as a practice of managing
|
||
error. The practitioners we interviewed are aware of the error-prone
|
||
nature of their systems but know it will never be perfect, and see it as
|
||
a key metric that needs to be acted upon.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>The most prominent way in which error figures in the diagrams is in
|
||
its quantified form of the true positive and false positive rates, TPR
|
||
and FPR. The significance and definition of these metrics is stressed by
|
||
CTO Gerwin van der Lugt (Diagram 6). In camera surveillance, the false
|
||
positive rate could be described as the number of fales positive
|
||
classifications relative to the number of video frames being analyzed.
|
||
Upon writing down these definitions, van der Lugt corrected his initial
|
||
definitions, as these definitions determine the work of his development
|
||
team, the ways in which his clients — security operators — engage with
|
||
the technology, and whether they perceive the output of the system as
|
||
trustworthy.</p>
|
||
</div>
|
||
<div data-custom-style="Figure">
|
||
<p><img src="assets//media/image8.png"
|
||
style="width:4.36111in;height:2.29028in" /></p>
|
||
</div>
|
||
<div data-custom-style="Caption">
|
||
<p>Diagram 6. Gerwin van der Lugt corrects his initial definitions of
|
||
the true positive and false positive rates, and stresses the importance
|
||
of their precise definition.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>The figuration of algorithmic security vision as inherently imprecise
|
||
affects the operationalization of security practices. Van der Lugt’s
|
||
example concerns whether the violence detection algorithm developed by
|
||
Oddity.ai should be trained to categorize friendly fighting
|
||
(<em>stoeien</em>) between friends as “violence” or not. In this
|
||
context, van der Lugt finds it important to differentiate what counts as
|
||
false positive in the algorithm’s evaluation metric from an error in the
|
||
algorithm’s operationalization of a security question.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>He gives two reasons to do so. First, he anticipates that the
|
||
exclusion of <em>stoeien</em> from the category of violence would
|
||
negatively impact TPR. In the iterative development of self-learning
|
||
systems, the TPR and FPR, together with the true and false
|
||
<em>negative</em> rates must perform a balancing act. Van der Lugt
|
||
outlines that with their technology they aim for fewer than 100 false
|
||
positives per 100 million frames per week. The FPR becomes indicative of
|
||
the algorithm’s quality, as too many faulty predictions will desensitize
|
||
the human operator to system alerts.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>This leads to van der Lugt’s second point: He fears that the
|
||
exclusion of <em>stoeien</em> from the violence category might cause
|
||
unexpected biases in the system. For example, instead of distinguishing
|
||
violence from <em>stoeien</em> based on people’s body movements, the
|
||
algorithm might make the distinction based on their age. For van der
|
||
Lugt, this would be an undesirable and hard to notice form of
|
||
discrimination. In developing algorithmic (in)security, error is figured
|
||
not merely as a mathematical concept but (as shown in Diagram 6) as a
|
||
notion that invites pre-emption — a mitigation of probable failure — for
|
||
which the developer is responsible. The algorithmic condition of
|
||
security vision is figured as the pre-emption of error.</p>
|
||
</div>
|
||
<div data-custom-style="Figure">
|
||
<p><img src="assets//media/image9.png"
|
||
style="width:3.91944in;height:3.06806in" /></p>
|
||
</div>
|
||
<div data-custom-style="Caption">
|
||
<p>Diagram 7. By drawing errors on a timeline, van Rest calls attention
|
||
to the pre-emptive nature of error in the development process of
|
||
computer vision technologies.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>According to critical AI scholar Matteo Pasquinelli, “machine
|
||
learning is technically based on formulas for error correction” (2019:
|
||
2). Therefore, any critical engagement with such algorithmic processes
|
||
needs to go beyond citing errors, “for it is precisely through these
|
||
variations that the algorithm learns what to do” (Amoore, 2019: 164),
|
||
pushing us to reconsider any argument based on the inaccuracy of the
|
||
systems.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>The example of <em>stoeien</em> suggests that it is not so much a
|
||
question if, or how much, these algorithms err, but how these errors are
|
||
anticipated and negotiated. Thus, taking error as a hallmark of machine
|
||
learning we can see how practices of (in)security become shaped by the
|
||
notion of mathematical error well beyond their development stages. Error
|
||
figures centrally in the development, acquisition and deployment of such
|
||
devices. As one respondent indicated, predictive devices are inherently
|
||
erroneous, but the quantification of their error makes them amenable to
|
||
"risk management.”</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>While much has been written about security technologies as a device
|
||
<em>for</em> risk management, little is known about how security
|
||
technologies are conceptualized as objects <em>of</em> risk management.
|
||
What happens then in this double relation of risk? The figure of the
|
||
error enters the diagrams as a mathematical concept, throughout the
|
||
conversations we see its figure permeate the discourse around
|
||
algorithmic security vision. By figuring algorithmic security vision
|
||
through the notion of error, risk is placed at the heart of the security
|
||
apparatus.</p>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<section
|
||
id="con-figurations-of-algorithmic-security-vision-fragmenting-accountability-and-expertise"
|
||
class="level1">
|
||
<h1>Con-figurations of algorithmic security vision: fragmenting
|
||
accountability and expertise</h1>
|
||
<div data-custom-style="Body Text">
|
||
<p>In the previous section we explored the changing <em>figurations</em>
|
||
of key dimensions of algorithmic security vision, in this section we
|
||
examine how these figurations <em>configure</em>. For Suchman, working
|
||
with configurations highlights “the histories and encounters through
|
||
which things are figured <em>into meaningful existence</em>, fixing them
|
||
through reiteration but also always engaged in ‘the perpetuity of coming
|
||
to be’ that characterizes the biographies of objects as well as
|
||
subjects” (Suchman, 2012: 50, emphasis ours) In other words, we are
|
||
interested in the practices and tensions that emerge as figurations
|
||
become embedded in material practices. We focus on two con-figurations
|
||
that emerged in the interviews: the delegation of accountability to
|
||
externally managed benchmarks, and the displacement of responsibility
|
||
through the reconfiguration of the human-in-the-loop.</p>
|
||
</div>
|
||
<section id="delegating-accountability-to-benchmarks" class="level2">
|
||
<h2>Delegating accountability to benchmarks</h2>
|
||
<div data-custom-style="Body Text">
|
||
<p>The first configuration is related to the evaluation of the error
|
||
rate in the training of algorithmic vision systems: it involves
|
||
datasets, benchmark institutions, and the idea of fairness as equal
|
||
representation among different social groups. Literature on the ethical
|
||
and political effects of algorithmic vision has notoriously focused on
|
||
the distribution of errors, raising questions of ethnic and racial bias
|
||
(e.g. Buolamwini and Gebru, 2018). Our interviews reflect the concerns
|
||
of much of this literature as the pre-emption of error figured
|
||
repeatedly in relation to the uneven distribution of error across
|
||
minorities or groups. In Diagram 8, Ádám Remport draws how different
|
||
visual traits have often led to different error rates. While the general
|
||
error metric of an algorithmic system might seem "acceptable," it
|
||
actually privileges particular groups, which is invisible when only the
|
||
whole is considered. Jeroen van Rest distinguishes such errors from the
|
||
inherent algorithmic imprecision in deep machine learning models, as
|
||
systemic biases (Diagram 7), as they perpetuate inequalities in the
|
||
society in which the product is being developed.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>To mitigate these concerns and manage their risk, many of our
|
||
interviewees who develop and implement these technologies, externalize
|
||
the reference against which the error is measured. They turn to a
|
||
benchmark run by the American National Institute of Standards and
|
||
Technology (NIST), which ranks facial recognition technologies by
|
||
different companies by their error metric across groups. John Riemen,
|
||
who is responsible for the use of forensic facial recognition technology
|
||
at the Center for Biometrics of the Dutch police, describes how their
|
||
choice for software is driven by a public tender that demands a "top-10"
|
||
score on the NIST benchmark. The mitigation of bias is thus outsourced
|
||
to an external, and in this case foreign, institution.</p>
|
||
</div>
|
||
<div data-custom-style="Figure">
|
||
<p><img src="assets//media/image10.png"
|
||
style="width:6.05417in;height:2.16389in" /></p>
|
||
</div>
|
||
<div data-custom-style="Caption">
|
||
<p>Diagram 8. Ádám Remport describes that facial recognition
|
||
technologies are often most accurate with white male adult faces,
|
||
reflecting the datasets they are trained with. The FPR is higher with
|
||
people with darker skin, children, or women, which may result in false
|
||
flagging and false arrests.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>We see in this outsourcing of error metrics a form of delegation that
|
||
brings about a specific regime of (in)visibility. While a particular
|
||
kind of algorithmic bias is rendered central to the NIST benchmark, the
|
||
mobilization of this reference obfuscates questions on how that metric
|
||
was achieved. That is to say, questions about training data are
|
||
invisibilized, even though that data is a known site of contestation.
|
||
For example, the NIST benchmark datasets are known to include faces of
|
||
wounded people (Keyes, 2019). The Clearview company is known to use
|
||
images scraped illegally from social media, and IBM uses a dataset that
|
||
is likely in violation of European GDPR legislation (Bommasani et al.,
|
||
2022: 154). Pasquinelli (2019) argued that machine learning models
|
||
ultimately act as data compressors: enfolding and operationalizing
|
||
imagery of which the terms of acquisition are invisibilized.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Attention to this invisibilization reveals a discrepancy between the
|
||
developers and the implementers of these technologies. On the one hand,
|
||
the developers we interviewed expressed concerns about how their
|
||
training data is constituted to gain a maximum false positive rate/true
|
||
positive rate (FPR/TPR) ratio, while showing concern for the legality of
|
||
the data they use to train their algorithms. On the other hand,
|
||
questions about the constitution of the dataset have been virtually
|
||
non-existent in our conversations with those who implement software that
|
||
relies on models trained with such data. Occasionally this knowledge was
|
||
considered part of the developers' intellectual property that had to be
|
||
kept a trade secret. A high score on the benchmark is enough to pass
|
||
questions of fairness, legitimizing the use of the algorithmic model.
|
||
Thus, while indirectly relying on the source data, it is no longer
|
||
deemed relevant in the consideration of an algorithm. This illustrates
|
||
well how the invisibilization of the “compressed” dataset, in
|
||
Pasquinelli’s terms, into a model, with the formalization of guiding
|
||
metrics into a benchmark, permits a bracketing of accountability. One
|
||
does not need to know how outcomes are produced, as long as the
|
||
benchmarks are in order.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>The configuration of algorithmic vision’s bias across a complex
|
||
network of fragmented locations and actors, from the dataset, to the
|
||
algorithm, to the benchmark institution reveals the selective processes
|
||
of (in)visibilization. This opens up fruitful alleys for new empirical
|
||
research: What are the politics of the benchmark as a mechanism of
|
||
legitimization? How does the outsourcing of assessing the error
|
||
distribution impact attention to bias? How has the critique of bias been
|
||
institutionalized by the security industry, resulting in the
|
||
externalization of accountability, through dis-location and
|
||
fragmentation?</p>
|
||
</div>
|
||
</section>
|
||
<section id="reconfiguring-the-human-in-the-loop" class="level2">
|
||
<h2>Reconfiguring the human-in-the-loop</h2>
|
||
<div data-custom-style="Body Text">
|
||
<p>A second central question linked to the delegation of accountability
|
||
is the configuration in which the security operator is located. The
|
||
effects of delegation and fragmentation in which the mitigation of
|
||
algorithmic errors is outsourced to an external party, becomes visible
|
||
in the ways in which the role of the security operator is configured in
|
||
relation to the institution they work for, the software’s assessment,
|
||
and the affected publics.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>The public critique of algorithms has often construed the
|
||
<em>human-in-the loop</em> as one of the last lines of defense in the
|
||
resistance to automated systems, able to filter and correct erroneous
|
||
outcomes (Markoff, 2020). The literature in critical security studies
|
||
has however problematized the representation of the security operator in
|
||
algorithmic assemblages by discussing how the algorithmic predictions
|
||
appear on their screen (Aradau and Blanke, 2018), and how the embodied
|
||
decision making of the operator is entangled with the algorithmic
|
||
assemblage (Wilcox, 2017). Moreover, the operator is often left guessing
|
||
at the working of the device that provides them with information to make
|
||
their decision (Møhl, 2021).</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>What our participants’ diagrams emphasized is how a whole spectrum of
|
||
system designs emerges in response to similar questions, for example the
|
||
issue of algorithmic bias. A primary difference can be found in the
|
||
degree of understanding of the systems that is expected of security
|
||
operators, as well as their perceived autonomy. Sometimes, the human
|
||
operator is central to the system’s operation, forming the interface
|
||
between the algorithmic systems and surveillance practices. Gerwin van
|
||
der Lugt, developer of software at Oddity.ai that detects criminal
|
||
behavior argues that “the responsibility for how to deal with the
|
||
violent incidents is always [on a] human, not the algorithm. The
|
||
algorithm just detects violence—that’s it—but the human needs to deal
|
||
with it.”</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Dirk Herzbach, chief of police at the Police Headquarters Mannheim,
|
||
adds that when alerted to an incident by the system, the operator
|
||
decides whether to deploy a police car. Both Herzbach and Van der Lugt
|
||
figure the human-in-the-loop as having full agency and responsibility in
|
||
operating the (in)security assemblage (cf. Hoijtink and Leese,
|
||
2019).</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Some interviewees drew a diagram in which the operator is supposed to
|
||
be aware of the ways in which the technology errs, so they can address
|
||
them. Several other interviewees considered the technical expertise of
|
||
the human-in-the-loop to be unimportant, even a hindrance.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Chief of police Herzbach prefers an operator to have patrol
|
||
experience to assess which situations require intervention. He is
|
||
concerned that knowledge about algorithmic biases might interfere with
|
||
such decisions. In the case of the Moscow metro, in which a facial
|
||
recognition system has been deployed to purchase tickets and open access
|
||
gates, the human-in-the-loop is reconfigured as an end user who needs to
|
||
be shielded from the algorithm’s operation (c.f. Lorusso, 2021). On
|
||
these occasions, expertise on the technological creation of the suspect
|
||
becomes fragmented.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>These different figurations of the security operator are held
|
||
together by the idea that the human operator is the expert of the
|
||
subject of security, and is expected to make decisions independent from
|
||
the information that the algorithmic system provides.</p>
|
||
</div>
|
||
<div data-custom-style="Figure">
|
||
<p><img src="assets//media/image11.png"
|
||
style="width:5.80694in;height:2.79375in" /></p>
|
||
</div>
|
||
<div data-custom-style="Caption">
|
||
<p>Diagram 9. Riemen explains the process of information filtering that
|
||
is involved in querying the facial recognition database of the Dutch
|
||
police.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Other drivers exist, however, to shield the operator from the
|
||
algorithm’s functioning, challenging individual expertise and
|
||
acknowledging the fallibility of human decision making. In Diagram 9,
|
||
John Riemen outlines the use of facial recognition by the Dutch police.
|
||
He describes how data from the police case and on the algorithmic
|
||
assessment is filtered out as much as possible from the information
|
||
provided to the operator. This, Riemen suggests, might reduce bias in
|
||
the final decision. He adds that there should be no fewer than three
|
||
humans-in-the-loop who operate independently to increase the accuracy of
|
||
the algorithmic security vision.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>Instead of increasing their number, there is another configuration of
|
||
the human-in-the-loop that responds to the fallibility of the operator.
|
||
For the Burglary-Free Neighborhood project in Rotterdam, project manager
|
||
Guido Delver draws surveillance as operated by neighborhood residents,
|
||
through a system that they own themselves. By involving different
|
||
stakeholders, Delver hopes to counter government hegemony over the
|
||
surveillance apparatus. However, residents are untrained in assessing
|
||
algorithmic predictions raising new challenges. Delver illustrates a
|
||
scenario in which the algorithmic signaling of a potential burglary may
|
||
have dangerous consequences: “Does it invoke the wrong behavior from the
|
||
citizen? [They could] go out with a bat and look for the guy who has
|
||
done nothing [because] it was a false positive.” In this case, the worry
|
||
is that the erroneous predictions will not be questioned. Therefore, in
|
||
Delver’s project the goal was to actualize an autonomous system, “with
|
||
as little interference as possible.” Human participation or
|
||
“interference” in the operation are potentially harmful. Thus, figuring
|
||
the operator — whether police officer or neighborhood resident — as
|
||
risky, can lead to the relegation of direct human intervention.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>By looking at the figurations of the operator that appear in the
|
||
diagrams we see multiple and heterogeneous configurations of
|
||
regulations, security companies, and professionals. In each
|
||
configuration, the human-in-the-loop appears in different forms. The
|
||
operator often holds the final responsibility in the ethical functioning
|
||
of the system. At times they are configured as an expert in
|
||
sophisticated but error-prone systems; at others they are figured as end
|
||
users who are activated by the alerts generated by the system, and who
|
||
need not understand how the software works and errs, or who can be left
|
||
out.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>These configurations remind us that there cannot be any theorization
|
||
of “algorithmic security vision,” both of its empirical workings and its
|
||
ethical and political consequences without close attention to the
|
||
empirical contexts in which the configurations are arranged. Each
|
||
organization of datasets, algorithms, benchmarks, hardware and operators
|
||
has specific problems. And each contains specific politics of
|
||
visibilization, invisibilization, responsibility and accountability.</p>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<section id="a-diagram-of-research" class="level1">
|
||
<h1>A diagram of research</h1>
|
||
<div data-custom-style="Body Text">
|
||
<p>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.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>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.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>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.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>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.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>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.</p>
|
||
</div>
|
||
<div data-custom-style="Body Text">
|
||
<p>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 <em>program</em>, our interviews point toward a larger
|
||
research <em>diagram</em> of interrelated questions, which invites us to
|
||
think in terms of pathways through this dynamic and evolving network of
|
||
relations.</p>
|
||
</div>
|
||
</section>
|
||
<section id="interviewees" class="level1">
|
||
<h1>Interviewees</h1>
|
||
<ul>
|
||
<li><div data-custom-style="Normal">
|
||
<p>Gerwin van der Lugt develops software that detects “high-impact
|
||
crimes” in camera streams.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>András Lukács is a senior researcher and coordinator of the AI Lab at
|
||
the Department of Mathematics of the Eötvös Loránd University in
|
||
Budapest, and an AI adviser for the Hungarian Ministry of Technology and
|
||
Innovation.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>Guido Delver is an engineer and coordinator of the Rotterdam-based
|
||
Burglary-Free Neighborhood that builds autonomous systems into street
|
||
lamps to reinforce public security.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>Attila Batorfy is a journalist and data visualization expert who
|
||
teaches journalism, media studies and information graphics at the Media
|
||
Department of Eötvös Loránd University.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>Peter Smith is a senior security expert working for a European
|
||
organization employing border technologies.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>Adam Remport is a Hungarian legal expert and activist working on
|
||
state actors’ use of biometric technologies.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>John Riemen is head of the Center for Biometricts for the Dutch
|
||
police.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>Jeroen van Rest is a safety expert and senior consultant in
|
||
risk-based security at TNO, the Netherlands.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>Two anonymous respondents involved in the Living Lab Scheveningen, in
|
||
the Hague.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>Sergei Miliaev is principal researcher and facial recognition team
|
||
lead at VisionLabs in Amsterdam.</p>
|
||
</div></li>
|
||
<li><div data-custom-style="Normal">
|
||
<p>Dirk Herzbach is chief of police of the Polizeipräsidium
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||
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|
||
</div></li>
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||
</ul>
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||
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</div>
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</section>
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||
<section class="footnotes footnotes-end-of-document"
|
||
role="doc-endnotes">
|
||
<hr />
|
||
<ol>
|
||
<li id="fn1" role="doc-endnote"><div data-custom-style="Footnote Text">
|
||
<p><span data-custom-style="Footnote Characters"></span> The interface
|
||
software and code is available at <a
|
||
href="https://git.rubenvandeven.com/security_vision/svganim"><span
|
||
data-custom-style="Hyperlink">https://git.rubenvandeven.com/security_vision/svganim</span></a>
|
||
and <a href="https://gitlab.com/security-vision/chronodiagram"><span
|
||
data-custom-style="Hyperlink">https://gitlab.com/security-vision/chronodiagram</span></a></p>
|
||
</div>
|
||
<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></li>
|
||
<li id="fn2" role="doc-endnote"><div data-custom-style="Footnote Text">
|
||
<p><span data-custom-style="Footnote Characters"></span> 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.</p>
|
||
</div>
|
||
<a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></li>
|
||
<li id="fn3" role="doc-endnote"><div data-custom-style="Footnote Text">
|
||
<p><span data-custom-style="Footnote Characters"></span> 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.</p>
|
||
</div>
|
||
<a href="#fnref3" class="footnote-back" role="doc-backlink">↩︎</a></li>
|
||
<li id="fn4" role="doc-endnote"><div data-custom-style="Footnote Text">
|
||
<p><span data-custom-style="Footnote Characters"></span> 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.</p>
|
||
</div>
|
||
<a href="#fnref4" class="footnote-back" role="doc-backlink">↩︎</a></li>
|
||
</ol>
|
||
</section>
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||
</body>
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