The research on technologies and methodologies for (accurate, real-time, spontaneous, three-dimensional…) facial expression recognition is ongoing and has been fostered in the past decades by advances in classification algorithms like deep learning, which makes them part of the Artificial Intelligence literature. Still, despite its upcoming application to contexts such as human–computer interaction, product and service design, and marketing, only a few literature studies have investigated the willingness of end users to share their facial data with the purpose of detecting emotions. (...) This study investigates the level of awareness and interest of 373 potential consumers towards this technology in the car insurance sector, particularly in the contract drafting phase, with a focus on differentiating the respondents between generation Y and Z. Results show that younger people, individuals with higher levels of education, and social network users feel more confident about this innovative technology and are more likely to share their expressive facial data. (shrink)

This article examines discourses about mixed reality as a data-rich sensing technology – specifically, engaging with discourses of time as framed by developers, engineers and in corporate PR and marketing in a range of public facing materials. We focus on four main settings in which mixed reality is imagined to be used, and in which time was a dominant discursive theme – the development of mixed reality by big tech companies, the use of mixed reality for defence, mixed reality as (...) a technology for control of populations in civil society and mixed reality as a technology used in workplace settings. Across these settings, the broad narrative is that mixed reality technologies afford overwhelmingly positive benefits like efficiency and security through their capture, relay and rendition of data – affording a form of anticipatory power to the user. The framing of temporality, we argue, is underlain by social and political values, which represent certain interests, but leave others out in the imagination of mixed reality's technological advance. (shrink)

Increasing computational power and improving deep learning methods have made computer vision technologies pervasively common in urban environments. Their applications in policing, traffic management, and documenting public spaces are increasingly common (Ridgeway 2018, Coifman et al. 1998, Sun et al. 2020). Despite the often-discussed biases in the algorithms' training and unequally borne benefits (Khosla et al. 2012), almost all applications similarly reduce urban experiences to simplistic, reductive, and mechanistic measures. There is a lack of context, depth, and specificity in these (...) practices that enables semantic knowledge or analysis within urban contexts, especially within the context of using and occupying urban space. This paper will critique existing uses of artificial intelligence and computer vision in urban practices to propose a new framework for understanding people, action, and public space. This paper revisits Geertz's (1973) use of thick descriptions in generating interpretive theories of culture and activity and uses this lens to establish a framework to approach evaluating the varied uses of computer vision technologies that weigh meaning. By discussing cases of implemented examples of urban computer vision—from LinkNYC and Numina's urban measurements to the Detroit Police's use of DataWorks Plus's facial recognition technology—it proposes a framework for evaluating the thickness of the algorithm's conclusions against the computational method's complexity required to produce that outcome. Further, we discuss how the framework's positioning may differ (and conflict) between different users of the technology, from engineer to urban planner and policymaker, to citizen. This paper also discusses how the current use and training of deep learning algorithms and how this process limits semantic learning and proposes three potential methodologies toward gaining a more contextually specific, urban-semantic, description of urban space relevant to urbanists. This paper contributes to the critical conversations regarding the proliferation of artificial intelligence by challenging the current applications of these technologies in the urban environment by highlighting their failures within this context while also proposing an evolution of these algorithms that may ultimately make them sensitive and useful within this spatial and cultural milieu. (shrink)