Big data biology – bioinformatics, computational biology, systems biology (including “omics”), and synthetic biology – raises a number of issues for the philosophy of science. This article deals with several, such as: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data “speak for themselves?” I discuss these issues by way of a reflection on Carl Woese’s worry that “a society that permits biology to become an engineering discipline, that allows science to slip into the role of changing the living world without trying to understand it, is a danger to itself.” And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena. Relevance: Many interesting things can be learned about the irreducibly human nature of scientific knowledge in a perspectivist stance (“view from somewhere”) while avoiding futile constructivism vs. realism debates. Qua perspectivists, constructive empiricists à la Van Fraassen and constructive realists à la Giere can cooperate in a profitable way.

Although information and communication technologies (ICT) have created hope for a shared pluralistic world, democratic principles are far from being respected in the public digital environment, and require a detailed knowledge of the laws by which they are governed. Von Foerster’s conjecture is one of the early theoretical results that could help to understand these laws. Although neglected for a long time, the advent of the overlying layer of recommendation and ranking systems which is progressively occupying the web has given empirical evidences of this conjecture, which predicts the consequences of increasing inter-individual influences on social dynamics and the susceptibility of these latter to manipulation. With both von Foerster’s conjecture and the Snowden revelations in the background, we analyse the impact of ICTon human societies and their governance, in view of the fact that they have a massive impact on the way in which people influence each other in their tastes and actions.

Key words: Information and technology governance, social media, Big Data, information and communication technologies, Snowden, revelations, ranking systems, recommendation, social networks

Discourse about smart algorithms and digital social agents still refers primarily to the construction of artificial intelligence that reproduces the faculties of individuals. Recent developments, however, show that algorithms are more efficient when they abandon this goal and try instead to reproduce the ability to communicate. Algorithms that do not “think” like people can affect the ability to obtain and process information in society. Referring to the concept of communication in Niklas Luhmann’s theory of social systems, this paper critically reconstructs the debate on the computational turn of big data as the artificial reproduction not of intelligence but of communication. Self-learning algorithms parasitically take advantage – be it consciously or unaware – of the contribution of web users to a “virtual double contingency.” This provides society with information that is not part of the thoughts of anyone, but, nevertheless, enters the communication circuit and raises its complexity. The concept of communication should be reconsidered to take account of these developments, including (or not) the possibility of communicating with algorithms.

Key words: social algorithms, big data, artificial intelligence, neural networks, deep learning, double contingency, systems theory.