I am super excited that the paper I have co-authored with Hendrik Heuer and Andreas Breiter on the problematic framing of Machine Learning in tutorials has been published in Big Data & Society. Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. In the paper we show what this difference means for the critical analysis of socio-technical systems based on machine learning. To provide a foundation for future critical analysis of machine learning-based systems, we engage with how the term is framed and constructed in self-education resources. For this, we analyze machine learning tutorials, an important information source for self-learners and a key tool for the formation of the practices of the machine learning community. Our analysis identifies canonical examples of machine learning as well as important misconceptions and problematic framings. Our results show that machine learning is presented as being universally applicable and that the application of machine learning without special expertise is actively encouraged. Explanations of machine learning algorithms are missing or strongly limited. Meanwhile, the importance of data is vastly understated. This has implications for the manifestation of (new) social inequalities through machine learning-based systems.

The interdisciplinary Socio-Gerontechnology Network attends to questions of the use and design of gerontechnologies from a critical social science and design perspective. The network has now published its first edited volume: Socio-gerontechnology – Interdisciplinary Critical Studies of Ageing and Technology, which brings together perspectives from ageing studies and science and technology studies. I am have contributed a contribution with Andreas Bischof (Technical University of Chemnitz) which is entitled: Configuring the older adult – How age and ageing are re-configured in gerontechnology design. The chapter will be available open access in due course.

Chapter abstract: Later life has become one of the most prominent topics for the design and development of digital technologies, resulting in the creation of a large body of prototypes and products (gerontechnologies). However, the majority of these efforts suffer from a lack of empirical grounding of their imaginaries of ageing and later life while also failing to involve older adults in design processes in meaningful ways. This chapter reviews how age and ageing can be configured across different instances of the development and deployment of digital technologies. We understand such design processes as configuration practices that co-construct older users and later life. By using the concept of re-con-figuration, we critically reflect about conceptual, ethical and pragmatic challenges of involving older people in design processes.

The Sociolog of the Sciences Yearbook is now online and Open Access. It’s editors Karen Kastenhofer and Susan Molyneux-Hodgson have assembled an interesting collection of 13 contributions across a wide range of technosciences.

My contribution looks at ways in which the concept of “communities of practice” has been appropriated and argues that objects such as templates are not simply a means to share practices in digital communities but rather a means for enacting membership.

Andreas Breiter and I edited a special issue on the datafication in education. It appeared in the journal Learning, Media and Technology. The special issue sheds light on the dynamics of datafication and related transformation of education. Contributions consider data practices that span across different countries, educational fields and governance levels from early childhood education (Bradbury), to schools (Ratner et al; Manolev et al), universities (Jones & McCoy), educational technology providers (Macgilchrist) to educational policy making and governance (Williamson & Piattoeva).

In sum, digital data allow for the analysis of different educational practices to a degree of complexity not previously possible and to a much greater extent, as they can be very detailed, cover a more complete scope and can be flexibly combined. This is increasingly happening in real time due to the power of computers and algorithms. In the near future, sensors will provide further data. As such digital data not only serve to support decisions, but also fundamentally change the organisation of learning and teaching. Thus, digital data not only support decision-making (“data-driven decision making”), but also fundamentally change the organisation of learning and teaching. These transformation processes lead to partly ambivalent consequences, such as new possibilities for participation, but also the monitoring and emergence/manifestation of inequalities.

The editorial can be accessed here, an overview on all contributions here.