The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications (...) of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: informed consent, privacy, ownership, epistemology and objectivity, and ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: the dangers of ignoring group-level ethical harms; the importance of epistemology in assessing the ethics of Big Data; the changing nature of fiduciary relationships that become increasingly data saturated; the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; future problems with ownership of intellectual property generated from analysis of aggregated datasets; and the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices. (shrink)

This article discusses three dimensions of creativity: metaphorical thinking; social interaction; and going beyond extrapolation in predictions. An overview of applications of neural networks in these three areas is offered. It is argued that the current reliance on the apparatus of statistical regression limits the scope of possibilities for neural networks in general, and in moving towards artificial creativity in particular. Artificial creativity may require revising some foundational principles on which neural networks are currently built.

We expect Big Data methods to contribute to research with results that are not inferior to those attained in other ways but possibly better, or hard or impossible to generate in other ways. Those who apply these methods may also aspire to augment the arsenal of research methods, offer surrogates for existing research designs, and re-orient research. Moreover, we can critically examine the institutional, societal and political effects of the Big Data methods and the conditions for the solid institutionalization of (...) these methods in social and political research. To reach its primary objective, this article elaborates conclusions on how Big Data methods, not only by means of their ‘social life’ but also by their ‘political life’, may influence the institutionalization of social and political research. To reach its secondary objective, the article re-examines a study of budgetary legislation in 13 countries carried out by means of Big Data methods to draw conclusions concerning the augmentation of the arsenal of research methods, the surrogation of existing research designs, and the re-orientation of research. (shrink)

Emotion research that attends to the cultural dynamics of affective life remains underdeveloped. I outline an agenda for an understudied phenomenon that can orient emotion researchers to the situated, cultural practices of affective life: Neo-emotions. Neo-emotions, when situated within macro-level processes and cultural events, illustrate the constrained yet creative practices that social actors use to address the disconnect between one's emotional vocabulary and dynamic environment. As such, neo-emotions are analytically rich cultural practices that can be empirically explored through sociological, anthropological, (...) historical, and psychological inquiry. I discuss a range of neo-emotions, including doomscrolling, eco-grief, and Black joy, their social antecedents (digitalization, disasters/crises, and social movements) and methodological implications for an interdisciplinary agenda. (shrink)

Working with computational methods and large textual analysis has been challenging and very rewarding—with all the ups and downs that doing empirical social research entails. In my contribution, I relate some research experiences and reflect upon data construction and the links between theory, data, and methods.

Much of social life now takes place online, and records of online social interactions are available for social science research in the form of massive digital text archives. But cultural social science has contributed little to the development of machine-assisted text analysis methods. As a result few text analysis methods have been developed that link digital text data to theories about culture and discourse. This paper attempts to lay the groundwork for development of such methods by proposing metatheoretical and theoretical (...) foundations suitable for machine-assisted semantic text analysis. Metatheoretically I draw on the work of Elder-Vass, Kaidesoja and others to argue that digital text analysis methods ought to be based on a realist constructionist ontology that treats discourses as ontologically real emergent social entities that have causal relationships with non-discursive social and cognitive processes. Theoretically I follow Feldman and many others in arguing that language is fundamentally shaped by processes of embodied cognition. Researchers developing digital text analysis techniques must theoretically account for such processes if they wish to produce algorithms that can interpret texts in ways that supplement, and not only amplify, human interpretation. I critically survey contemporary text analysis methods that implicitly share these metatheoretical and theoretical positions and discuss some ways these can be further developed with newly available software. (shrink)

The proliferation of digital data has been the impetus for the emergence of a new discipline for the study of social life: ‘computational social science’. Much research in this field is founded on the premise that society is a complex system with emergent structures that can be modeled or reconstructed through digital data. This paper suggests that computational social science serves practical and legitimizing functions for digital capitalism in much the same way that neoclassical economics does for neoliberalism. In recognition (...) of this homology, this paper develops a critique of the complexity perspective of computational social science and argues for a heterodox computational social science founded on the meta-theory of critical realism that is critical, methodological pluralist, interpretative and explanative. This implies diverting computational social science’ computational methods and digital data so as to not be aimed at identifying invariant laws of social life, or optimizing state and corporate practices, but to instead be used as part of broader research strategies to identify contingent patterns, develop conjunctural explanations, and propose qualitatively different ways of organizing social life. (shrink)

This article examines regulation’s understanding of technology in American financial markets as means for rethinking the contours and institutional limits of governance in the age of financialization. The article identifies how the Securities and Exchange Commission perceived markets and their conceptual relation to technology throughout much of the long twentieth century by distilling the “ontologies” expressed by the agency’s leadership. Despite the fact that SEC’s commissioners recognized technologies as playing a central role in the market’s current and future operations, these (...) were never effectively brought under regulatory scrutiny even when such action fell under the commission's jurisdictional remit. Rather than regulating technologies as constitutive of markets, the governance of the material devices of finance was discursively kept at arm's-length by presenting them as intractable objects that were external to financial innovation. Triangulating across several techniques of computational text analysis, this article shows how this interpretation of technology mirrored distinct shifts in how regulators understood markets, from being physical trading sites populated by agents that required vetting and certification to a distributed, multi-sited system surveilled through transparency and disclosure. Throughout these ontological transitions, technology’s inscrutability remained, limiting the capacity of the state and its regulatory agencies to shape the evolution of finance and its underlying infrastructures. (shrink)