Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention towards the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the circulation and contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by networked publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images is limited. However, combining automated analyses of images - broken down by their compositional elements - with repurposing platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This paper explores the capacities of platform data - hashtag modularity and retweet counts - to complement the automated assessment of social media images; doing justice to both the visual elements of an image and the contextual elements encoded by networked publics that co-create meaning.
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Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.
The New Aesthetic and Art: Constellations of the Postdigital is an interdisciplinary analysis focusing on new digital phenomena at the intersections of theory and contemporary art. Asserting the unique character of New Aesthetic objects, Contreras-Koterbay and Mirocha trace the origins of the New Aesthetic in visual arts, design, and software, find its presence resonating in various kinds of digital imagery, and track its agency in everyday effects of the intertwined physical world and the digital realm. Contreras-Koterbay and Mirocha bring to light an original perspective that identifies an autonomous quality in common digital objects and examples of art that are increasingly an important influence for today’s culture and society.Influenced by a diverse range of figures, ranging from Vilém Flusser, Arthur Schopenhauer, Immanuel Kant, David Berry, Lev Manovich, Olga Goriunova, Ernst Mayr, Bruce Sterling and, of course, James Bridle, The New Aesthetic and Art: Constellations of the Postdigital doesn’t just propose a description of a new set of objects but radically asserts that New Aesthetic objects analogously function as organisms within a broader digital-physical ecosystems of things and agents.
