We introduce the working concept of “affective arrangement.” This concept is the centerpiece of a perspective on situated affectivity that emphasizes relationality, dynamics, and performativity. Our proposal relates to work in cultural studies and continental philosophy in the Spinoza–Deleuze lineage, yet it is equally geared to the terms of recent work in the philosophy of emotion. Our aim is to devise a framework that can help flesh out how affectivity unfolds dynamically in a relational setting by which it is at the same time modulated in recurring ways. With this orientation, this article contributes to the interdisciplinary study of situated affectivity and to the theoretical and conceptual unification of distinct strands of research from several disciplines.
Cities worldwide are increasingly becoming agents of climate change mitigation, while simultaneously aiming for other goals, such as improved accessibility and clean air. Based on stakeholder interviews and data analysis, we assess the current state of urban mobility in the four European cities of Barcelona, Malmö, Sofia and Freiburg. We then provide scenarios of increasingly ambitious policy packages, reducing greenhouse gas emissions from urban transport by up to 80% from 2010 to 2040. We find significant concurrent co-benefits in cleaner air, reduced noise ambience, fewer traffic-related injuries and deaths, more physical activity, less congestion and monetary fuel savings. Our scenarios suggest that non-motorized transport, especially bicycles, can occupy high modal shares, particularly in cities with less than 0.5 million inhabitants. We think that this kind of multi-criteria assessment of social costs and benefits is a useful complement to cost-benefit analysis of climate change mitigation measures.
Interactive social cognition theory and approaches of developmental psychology widely agree that central aspects of emotional and social experience arise in the unfolding of processes of embodied social interaction. Bi-directional dynamical couplings of bodily displays such as facial expressions, gestures, and vocalizations have repeatedly been described in terms of coordination, synchrony, mimesis, or attunement. In this paper, I propose conceptualizing such dynamics rather as processes of affective resonance. Starting from the immediate phenomenal experience of being immersed in interaction, I develop the philosophical notion of affective resonance to refer to a dynamic entanglement of moving and being-moved in relation.The concept of affective resonance makes visible that the interaction dynamic itself creates an affective experience rather than transmitting internal feeling states between pre-existent individuals. This leads to a philosophical framework in which relationality and ontogeny are primary over separate individuals, and in which the naturalistic distinction of a fundamental physical level versus an emerging level of social processes has to be given up.
Today, artificial intelligence (AI), especially machine learning, is structurally dependent on human participation. Technologies such as deep learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI.” This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control, and digital labor.
Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using data other individuals provide about themselves, possibly even anonymously.
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