This paper aims to provide new insights to debates on group privacy, which can be seen as part of a social turn in privacy scholarship. Research is increasingly showing that the classic individualistic understanding of privacy is insufficient to capture new problems in algorithmic and online contexts. An understanding of privacy as an “interpersonal boundary-control process” (Altman, The environment and social behavior, Brooks and Cole, Monterey, 1975) framing privacy as a social practice necessary to sustain intimate relationships is gaining ground. (...) In this debate, my research is focused on what I refer to as “self-determined groups” which can be defined as groups whose members consciously and willingly perceive themselves as being part of a communicative network. While much attention is given to new forms of algorithmically generated groups, current research on group privacy fails to account for the ways in which self-determined groups are affected by changes brought about by new information technologies. In an explorative case study on self-organized therapy groups, I show how these groups have developed their own approach to privacy protection, functioning on the basis of social practices followed by all participants. This informal approach was effective in pre-digital times, but online, privacy threats have reached a new level extending beyond the scope of a group’s influence. I therefore argue that self-determined sensitive topic groups are left facing what I present as a dilemma: a tension between the seemingly irreconcilable need for connectivity and a low threshold, on the one hand, and the need for privacy and trust, on the other. In light of this dilemma, I argue that we need new sorts of political solutions. (shrink)

It is well known that AI-based language technology—large language models, machine translation systems, multilingual dictionaries, and corpora—is currently limited to three percent of the world’s most widely spoken, financially and politically backed languages. In response, recent efforts have sought to address the “digital language divide” by extending the reach of large language models to “underserved languages.” We show how some of these efforts tend to produce flawed solutions that adhere to a hard-wired representational preference for certain languages, which we call (...) language modeling bias. Language modeling bias is a specific and under-studied form of linguistic bias were language technology by design favors certain languages, dialects, or sociolects with respect to others. We show that language modeling bias can result in systems that, while being precise regarding languages and cultures of dominant powers, are limited in the expression of socio-culturally relevant notions of other communities. We further argue that at the root of this problem lies a systematic tendency of technology developer communities to apply a simplistic understanding of diversity which does not do justice to the more profound differences that languages, and ultimately the communities that speak them, embody. Drawing on the concept of epistemic injustice, we point to the broader ethico-political implications and show how it can lead not only to a disregard for valuable aspects of diversity but also to an under-representation of the needs of marginalized language communities. Finally, we present an alternative socio-technical approach that is designed to tackle some of the analyzed problems. (shrink)