This article argues that the self-management of one’s privacy is impossible due to privacy externalities. Privacy externalities are the negative by-product of the services offered by some data controllers, whereby the price to ‘pay’ for a service includes not just the provision of the user’s own personal data, but also that of others. This term, related to similar concepts from the literature on privacy such as ‘networked privacy’ or ‘data pollution’, is used here to bring to light the incentives and (...) exploitative dynamics behind a phenomenon which, I demonstrate, benefits both the user and the data controller to the detriment of third-party data subjects. Building on these novel elements and on the relevant concepts and examples found in the existing literature, this article draws a comprehensive picture of the phenomenon, and offers two promising paths to address it—better enforcing the principle of data protection by design and by default, and relying on the framework of joint controllership. (shrink)

Recently, amid growing awareness that computer algorithms are not neutral tools but can cause harm by reproducing and amplifying bias, attempts to detect and prevent such biases have intensified. An approach that has received considerable attention in this regard is the Value Sensitive Design (VSD) methodology, which aims to contribute to both the critical analysis of (dis)values in existing technologies and the construction of novel technologies that account for specific desired values. This article provides a brief overview of the key (...) features of the Value Sensitive Design approach, examines its contributions to understanding and addressing issues around bias in computer systems, outlines the current debates on algorithmic bias and fairness in machine learning, and discusses how such debates could profit from VSD-derived insights and recommendations. Relating these debates on values in design and algorithmic bias to research on cognitive biases, we conclude by stressing our collective duty to not only detect and counter biases in software systems, but to also address and remedy their societal origins. (shrink)