The daring vision of using big data technology to substantially advance the scientific understanding of human nature, individually and socially, and possibly solve age-old challenges of bridging the subjective and objective sides of human nature, rests on substantial assumptions about the concept of a human being. The daring big data vision may at the same time, in itself, serve to change the very concept of a human, regardless of how well the vision’s assumptions and prospects hold up to scrutiny. This issue of the European Review presents an attempt to critically engage with the question of how this complex situation affects the content and prospects of the vision of reconsidering humanity with the help of big data. In this introduction, the landscape of the issue is sketched and some general remarks of where the emerging map might take future research are made. In general, even if the assumptions of the daring big data vision turn out wanting, pragmatic factors may very well transform our own image of ourselves to fit it.

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to non-human domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with non-humans may not make us less human, but they promise to challenge fundamental views of what it is to be human.

Since the introduction of new technologies, the deluge of neuroscientific data has been overwhelming. On one hand this new information has produced remarkable breakthroughs in our understanding of brain function and development as well as lifesaving treatments for trauma and disease. On the other hand, the lure and reward for explanations of mental phenomena in terms of simple, manipulable brain processes has led to questionable research methodologies and unsubstantiated claims. A more fundamental issue is raised by the attempt to explain consciousness by means of information, as proposed by the Information Integration Theory (IIT). While the models produced by this massive computation of data will no doubt improve our understanding of brain function and capacity, a strict information processing approach cannot address the problem of meaning. A solution to this problem demands an evolutionary, developmental, and dynamic account of an organism in its environment. Data analysis will play a role in this inclusive explanatory program, but explanation is insufficient by data alone.

The growth of continuously generated, large-scale datasets, and new analytics to handle them, has created expectations, in some quarters, that new insights can be generated that will help us address the biggest challenges that face us as a species and therefore can shape future societal outcomes. It is hoped that these new technologies will lead not just to new discoveries but also to new questions and thinking that will deliver significant scientific advances. Perhaps there will be some genuine scientific advances but since many of the challenges that face us reside in the human world and depend upon how humans behave, we need to turn to the humanities and the social sciences as well as the natural sciences and look at the role Big Data could play there in adding to, or shaping, our future. And here, what concerns us is not just the assumptions guiding the new analytic techniques for data mining, data merging, linking and analysis, born out of smarter AI algorithms, it is a more fundamental issue about the constraints and limitations of the kind of data inputs and outputs being appealed to in Big Data systems and whether they are well served to provide an understanding of the human world.

Science and art have long recognized that perceptual experience depends on the involvement of the experiencer. In art history, this idea is captured by Ernst Gombrich’s ‘beholder’s share’. In neuroscience, it traces to Helmholtz’s concept of ‘perception as inference’, which is enjoying renewed prominence in the guise of ‘prediction error minimization’ (PEM) or the ‘Bayesian brain’. The shared idea is that our perceptual experience – whether of the world, of ourselves, or of an artwork – depends on the active ‘top-down’ interpretation of sensory input. Perception becomes a generative act, in which perceptual, cognitive, affective, and sociocultural expectations conspire to shape the brain’s ‘best guess’ of the causes of sensory signals. In this article, I explore the parallels between the Bayesian brain and the beholders’ share, illustrated, somewhat informally, with examples from Impressionist, Expressionist, and Cubist art. By connecting phenomenological insights from these traditions with the cognitive neuroscience of predictive perception, I outline a reciprocal relationship in which art reveals phenomenological targets for neurocognitive accounts of subjectivity, while the concepts of predictive perception may in turn help make mechanistic sense of the beholder’s share. This is not standard neuroaesthetics – the attempt to discover the brain basis of aesthetic experience – nor is it any kind of neuro-fangled ‘theory of art’. It is instead an examination of one way in which art and brain science can be equal partners in revealing deep truths about human experience.

Big data and machine learning techniques are increasingly applied to creative tasks, often with strong reactions of both awe and concern. But we have to be careful about where to attribute the creative agency. Is it really the machine that paints like van Gogh, or is it a human that uses a high-level tool to impart one pattern upon another, based on her aesthetic preferences? In this paper, the author analyses the problem of machine creativity, focusing on four central themes: the inherent convergence of machine learning and big data techniques, their dependence on assumptions and incomplete data, the possibility of explorative search as a new creative paradigm, and the related problem of the opacity of results from such methods. The Google Deep Dream project is brought in as an example to illustrate the discussion. Information and complexity are brought into the discussion as central concepts for both creative processes and the resulting artefacts, concluding that the complexity of the interaction between the creative agent and the environment during the creative process is a crucial parameter for meaningful creative output. Based on the exposed limitations in current technologies, the author concludes that the principal creative agency still lies in the developers and users of the tools, not in the data processing itself. Human effort and input still matters. But we can take a constructive approach, regarding big data techniques as tools one order of magnitude more complex than what was available before, allowing artists to work with abstractions previously unfeasible for computational work.

Data aggregations are an under-acknowledged site of social relations. The social and technical specifics of how data aggregate are arenas for rich debates about how knowledge ought to be produced, and who should produce it. Consumer goods such as fitness trackers create conditions where data scientists or professional researchers are no longer the only ones making decisions about how to aggregate data. Users of these products also rework their data to discover something medically significant to them. These practices call attention to a modality of ‘scaling up’ datasets about a single person that is different from, and until recently largely invisible to, clinical approaches to big data, which privilege the creation of a ‘bird’s eye’ view across as many people. Both technical questions how to build these aggregations, and social questions of who should be involved, betray broader epistemological issues about how new knowledge is created from electronic devices.