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This article examines the datafication of ageing by drawing on a practice approach toward care. We describe the datafication of ageing as a matter of care, achieved through the local tinkering of actors – technology designers, care staff, older adults, and highlighting the practices necessary to develop, maintain and implement data infrastructures. This paper draws on research conducted in a qualitative interview study in a LTC facility that uses AI-supported sensors to detect, predict and alarm care staff about falls of older residents. 18 interviews with developers, staff, residents and interest groups were conducted, as well as 24 h of participant observation in the care facility. The results reveal how AI-development for older target groups is characterized by absent data on these populations. Designers turn to practices that decontextualize data from the realities of older adults, relying on domain experts or synthetic data. This decontextualization of data requires recontextualization, with staff and older residents ensuring that the system functions smoothly, adapting their behavior, protecting the system from making false decisions and making existing care arrangements 'fit' the databases used to monitor activities in these arrangements. The ambivalent position of older adults in this data assemblage is further highlighted, as their caring practices are made invisible by different actors through ageist stereotypes, positioning them as being too frail to understand and engage with the system. While their bodily behavior is core for the databases, their perspective on and engagements with the operating system are marginalized, rendering some aspects of ageing hyper-visible, and others invisible. [ABSTRACT FROM AUTHOR]

Secondary data collection practices are often opaque to platform users and researchers but known to shape individuals' life chances in significant and unequal ways. In this paper, we articulate a clear relationship between invisible, unwanted data collection and its adverse, downstream consequences for marginalized groups by examining instances in the criminal justice field where social media data function as criminal evidence. We show how social media, as a now common form of courtroom evidence, may structurally work against public defense attorneys and defendants with low socioeconomic status (SES). Drawing on casework interviews with public defenders in New York City, we illustrate the mechanisms by which low-SES criminal defendants are at a disadvantage through overbroad search warrants, asymmetrical cooperation, and prejudicial evidence. We discuss the lessons and implications of our case study for platform privacy and governance research and for the courts. [ABSTRACT FROM AUTHOR]