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Critical Data Studies (CDS) explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals' daily lives. CDS questions the many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles.

The consequences of the COVID-19 pandemic are still working through health systems worldwide, and further reflections about the nature of health and disease, and about how to design and implement effective public health interventions are much needed. For numerous diseases and conditions, as well as for COVID-19, our knowledge base is rich. We know a lot about the biology of the disease, and we have plenty of statistics that relate health to socio-economic factors. In this paper, we argue that we need to add a third dimension to this knowledge base, namely a thorough description of the lifeworld of health and disease, in terms of the mixed biosocial mechanisms that operate in it. We present the concepts of lifeworld and of mixed mechanisms, and then illustrate how they can be operationalised and measured through mixed methodologies that combine qualitative and quantitative approaches. Finally, we explain the complementarity of our approach with the biological and statistical dimensions of health and disease for the design of public health interventions.

AbstractThe experimental revolution in the social sciences is one of the most significant methodological shifts undergone by the field since the 'quantitative revolution' in the nineteenth century. One of the often valued features of social science experimentation is precisely the fact that there are (alleged) clear methodological rules regarding hypothesis testing that come from the methods of the natural sciences and from the methodology of RCTs in the biomedical sciences, and that allow for the adjudication among contentious causal claims. We examine critically this claim and argue that some current understandings of the practices that surround social science experimentation overestimate the degree to which experiments can actually fulfil this role as "objective" adjudicators, by neglecting the importance of shared background knowledge or assumptions and of consensus regarding the validity of the constructs involved in an experiment. We take issue with the way the distinction between internal and external validity is often used to comment on the inferential import of experiments, used both among practitioners and among philosophers of science. We describe the ways in which the more common (dichotomous) use of the internal/external distinction differs from Cook and Campbell's original methodological project, in which construct validity and the four-fold validity typology were all important in assessing the inferential import of experiments. We argue that the current uses of the labels internal and external, as applied to experimental validity, help to encroach a simplistic view on the inferential import of experiments that, in turn, misrepresents their capacity to provide objective knowledge about the causal relations between variables.

AbstractResearch in the health sciences has been highly successful in revealing the aetiologies of many morbidities, particularly those involving the microbiology of communicable disease. This success has helped form a narrative to be found in numerous public health documents, about interventions to reduce the burden of non‐communicable diseases (e.g., obesity or alcohol related pathologies). These focus on tackling the purported pathogenic factors causing the diseases as a means of prevention. In this paper, we argue that this approach has been sub‐optimal. The mechanisms of aetiology and of prevention are sometimes significantly different and failure to make this distinction has hindered efforts at preventing non‐communicable diseases linked to diet, exercise and alcohol consumption. We propose a sociological approach as an alternative based on social practice theory. (A virtual abstract for this paper can be found at:https://www.youtube.com/channel/UC_979cmCmR9rLrKuD7z0ycA).

AbstractWith the recent renewed interest in AI, the field has made substantial advancements, particularly in generative systems. Increased computational power and the availability of very large datasets has enabled systems such as ChatGPT to effectively replicate aspects of human social interactions, such as verbal communication, thus bringing about profound changes in society. In this paper, we explain that the arrival of generative AI systems marks a shift from 'interacting through' to 'interacting with' technologies and calls for a reconceptualization of socio-technical systems as we currently understand them. We dub this new generation of socio-technical systems synthetic to signal the increased interactions between human and artificial agents, and, in the footsteps of philosophers of information, we cash out agency in terms of 'poiêsis'. We close the paper with a discussion of the potential policy implications of synthetic socio-technical system.

AbstractThe need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate values and other normative considerations, such as intersectoral vulnerabilities, at critical stages of the whole process from design and implementation to use and assessment. To connect ethics and epistemology of AI, we perform a double shift of focus. First, we move from trusting the output of an AI system to trusting the process that leads to the outcome. Second, we move from expert assessment to more inclusive assessment strategies, aiming to facilitate expert and non-expert assessment. Together, these two moves yield a framework usable for experts and non-experts when they inquire into relevant epistemological and ethical aspects of AI systems. We dub our framework 'epistemology-cum-ethics' to signal the equal importance of both aspects. We develop it from the vantage point of the designers: how to create the conditions to internalize values into the whole process of design, implementation, use, and assessment of an AI system, in which values (epistemic and non-epistemic) are explicitly considered at each stage and inspectable by every salient actor involved at any moment.

Comme toute science, les sciences sociales cherchent à expliquer et à comprendre les phénomènes relevant de leur objet, ici les phénomènes sociaux. Quelles sont les causes du terrorisme ? Pourquoi l'économie allemande est-elle plus performante que celle de la France ? Quelles sont les raisons des différences de mortalité observées entre les trois Régions de la Belgique ? … Voilà une série de questions judicieuses que l'on peut se poser. Il s'agit chaque fois d'une explanation-seeking why question, selon Carl Hempel (1965), c'est-à-dire d'une question sur le 'pourquoi' des choses, requérant une explication. Pour tenter d'y répondre, les scientifiques ont mis au point diverses approches et méthodes quantitatives ou qualitatives. Quelle que soit la méthodologie, et elles sont nombreuses en sciences sociales, il s'agira toujours de confronter ses hypothèses, théories et modèles aux faits. L'observation des faits et l'évaluation de la pertinence de leur interprétation constituent ainsi un aspect important de la démarche scientifique. L'objet de cet article n'est pas de présenter un tour d'horizon des différentes approches scientifiques adoptées par les sciences sociales. Il faudrait un très gros volume pour le faire : voir par exemple l'ouvrage bien connu de Madeleine Grawitz (2000) à ce sujet. Plus modestement, ce texte vise à montrer, dans une perspective causale, les particularités, les incertitudes et les défis que soulève la modélisation en sciences sociales. Pour saisir plus aisément nos propos, des exemples seront fournis tout au long du présent article. Le premier auteur étant démographe, les exemples seront principalement tirés des sciences de la population. Nous traiterons d'abord brièvement, dans la section 2, du modèle en tant que représentation de la réalité. La section 3 examinera les différences entre l'approche expérimentale et l'approche non-expérimentale, sur le plan de la causalité. La section 4 portera ensuite sur la relation entre modèle et explication scientifique. La section 5 examinera un exemple pratique de modélisation en démographie, tandis que la section 6 posera la question de l'unicité ou non du modèle. La section 7 traitera d'un problème très actuel : du bon usage du Big Data dans la modélisation en sciences sociales. La section 8 examinera la relation entre explication, modèle et intervention politique. L'article se terminera par quelques remarques en guise de conclusion.

Comme toute science, les sciences sociales cherchent à expliquer et à comprendre les phénomènes relevant de leur objet, ici les phénomènes sociaux. Quelles sont les causes du terrorisme ? Pourquoi l'économie allemande est-elle plus performante que celle de la France ? Quelles sont les raisons des différences de mortalité observées entre les trois Régions de la Belgique ? … Voilà une série de questions judicieuses que l'on peut se poser. Il s'agit chaque fois d'une explanation-seeking why question, selon Carl Hempel (1965), c'est-à-dire d'une question sur le 'pourquoi' des choses, requérant une explication. Pour tenter d'y répondre, les scientifiques ont mis au point diverses approches et méthodes quantitatives ou qualitatives. Quelle que soit la méthodologie, et elles sont nombreuses en sciences sociales, il s'agira toujours de confronter ses hypothèses, théories et modèles aux faits. L'observation des faits et l'évaluation de la pertinence de leur interprétation constituent ainsi un aspect important de la démarche scientifique. L'objet de cet article n'est pas de présenter un tour d'horizon des différentes approches scientifiques adoptées par les sciences sociales. Il faudrait un très gros volume pour le faire : voir par exemple l'ouvrage bien connu de Madeleine Grawitz (2000) à ce sujet. Plus modestement, ce texte vise à montrer, dans une perspective causale, les particularités, les incertitudes et les défis que soulève la modélisation en sciences sociales. Pour saisir plus aisément nos propos, des exemples seront fournis tout au long du présent article. Le premier auteur étant démographe, les exemples seront principalement tirés des sciences de la population. Nous traiterons d'abord brièvement, dans la section 2, du modèle en tant que représentation de la réalité. La section 3 examinera les différences entre l'approche expérimentale et l'approche non-expérimentale, sur le plan de la causalité. La section 4 portera ensuite sur la relation entre modèle et explication scientifique. La section 5 examinera un exemple pratique de modélisation en démographie, tandis que la section 6 posera la question de l'unicité ou non du modèle. La section 7 traitera d'un problème très actuel : du bon usage du Big Data dans la modélisation en sciences sociales. La section 8 examinera la relation entre explication, modèle et intervention politique. L'article se terminera par quelques remarques en guise de conclusion.

One method for causal analysis in the social sciences is structural modeling. Structural models, as used in this article, model the (causal) mechanism for a social phenomenon by recursively decomposing the multivariate distribution of the variables of interest. Often, however, one does not achieve a complete decomposition in terms of single variables but in terms of "blocks" of variables only. Papers giving an overview of this issue are nevertheless rare. The purpose of this article is to categorize distinct types of block-recursivity and to examine, in a multidisciplinary perspective, the implications of block-recursivity for causal attribution. A probabilistic approach to causality is first developed in the framework of a structural model. The article then examines block-recursivity due to the presence of contingent conditions, of interaction, and of conjunctive causes. It also discusses causal attribution when information on the ordering of the variables is incomplete. The article concludes by emphasizing, in particular, the importance of properly specifying the population of reference.