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Far from business-as-usual, we explain how Big Tech is reshaping the traditional automotive industry by making the car “platform ready,” as imposed on the web before it. “Big Tech” platform companies like Alphabet (Google), Apple, and Amazon are deeply invested in the future of automobility-from developing car-specific interfaces and self-driving technology to establishing business partnerships with automakers.
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In its conclusion, we aim to open a conversation about the way technological advancements mark important sociocultural changes in sense-making processes, geographical imaginations and everyday life experiences. The critical analysis of these peculiarities leads us to argue for the potential of these innovative visions to reshape our visual culture. Exploring drone hobbyists’ and developers’ perspectives on drone usage and the visuals they generate, we identify and examine three frequently occurring characteristics of drone visuals: top-down views, 360-degree panoramic views and ‘classic’ landscape perspectives. This article is the first to draw specific attention to the compositional structure of drone visuals, combining social semiotic analysis with ethnographic insights to assess how they are changing the way we think about the world. Despite the growing use of camera-laden drones in a range of commercial and non-commercial activities, to date, little scholarly attention has been paid to the semiotics of drone visuals. We also systematically study the effect of data domain and model size.ĭrone visuals are rapidly becoming part of our sociocultural imaginaries, generating distinct images that differ from traditional visual conventions and producing unexpected perspectives of the world that reveal hidden aspects of our surroundings. To study the practical effectiveness and broad applicability of our proposed indicators to any visual system, we apply them to off-the-shelf models built using widely adopted model training paradigms which vary in their ability to whether they can predict labels on a given image or only produce the embeddings. Yet, we believe it is a necessary first step towards (1) facilitating the widespread adoption and mandate of the fairness assessments in computer vision research, and (2) tracking progress towards building socially responsible models. These indicators are part of an ever-evolving suite of fairness probes and are not intended to be a substitute for a thorough analysis of the broader impact of the new computer vision technologies. Our indicators use existing publicly available datasets collected for fairness evaluations, and focus on three main types of harms and bias identified in the literature, namely harmful label associations, disparity in learned representations of social and demographic traits, and biased performance on geographically diverse images from across the world.We define precise experimental protocols applicable to a wide range of computer vision models. To initiate an effort towards standardized fairness audits, we propose three fairness indicators, which aim at quantifying harms and biases of visual systems. Systematic diagnosis of fairness, harms, and biases of computer vision systems is an important step towards building socially responsible systems.
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Does everyone equally benefit from computer vision systems? Answers to this question become more and more important as computer vision systems are deployed at large scale, and can spark major concerns when they exhibit vast performance discrepancies between people from various demographic and social backgrounds.