The development and application of artificial intelligence (AI) technology has raised many concerns about privacy violations in the public. Thus, privacy-preserving computation technologies (PPCTs) have been developed, and it is expected that these new privacy protection technologies can solve the current privacy problems. By not directly using raw data provided by users, PPCTs claim to protect privacy in a better way than their predecessors. They still have technical limitations, and considerable research has treated PPCTs as a privacy-protecting tool and focused (...) on possible technical improvements. In this article, we argue that PPCTs cannot effectively protect privacy in the narrow sense due to their technical limitations. Moreover, although these shortcomings could be remedied, they still fall into a paradox because their aim is to protect privacy, but they could reveal private information from users. This paradox of privacy protection not only aggravates the social impact arising from AI privacy issues but may also make the current privacy protection lose its meaning, resulting in a situation in which privacy protection is useless. (shrink)

If I decide to disclose information about myself, this act can undermine other people’s ability to effectively conceal information about themselves. One case in point involves genetic information: if I share ‘my’ genetic information with others, I thereby also reveal genetic information about my biological relatives. Such dependencies are well-known in the privacy literature and are often referred to as ‘privacy dependencies’. Some take the existence of privacy dependencies to generate a moral duty to sometimes avoid sharing information about oneself. (...) If true, we argue, then it is sometimes justified for others to impose harm on the person sharing the information to prevent them from doing so. This is a highly revisionary implication. Hence, one must either endorse a highly revisionary view on what one may do to protect one’s privacy, or one must reject the view that privacy dependencies can be used to justify a moral duty that constrains choices about sharing information about oneself. (shrink)

Purpose Privacy has been understood as about one’s own information, information that is not one’s own is not typically considered with regards to an individual’s privacy. This paper aims to draw attention to this issue for conceptualizing privacy when one’s privacy is breached by others’ information. Design/methodology/approach To illustrate the issue that others' information can breach one's own privacy, this paper uses real-world applications of forensic genealogy and recommender systems to motivate the discussion. Findings In both forensic genealogy and recommender (...) systems, the individual’s privacy is breached by information that is not one’s own. The information that breached one’s privacy, by its nature, is beyond the scope of an individual, which is a phenomenon that has already been captured by emerging discussions about group privacy. This paper further argues that the underlying issue reflected by the examples of forensic genealogy is an extreme case even under the consideration of group privacy. This is because, unlike recommender systems that rely on large amounts of data to make inferences about an individual, forensic genealogy exposes one’s identity by using only one other individual’s information. This paper echoes existing discussions that this peculiar situation where others’ information breaches one’s own privacy reveals the problematic nature of conceptualizing privacy relying only on individualistic assumptions. Moreover, this paper suggests a relational perspective as an alternative for theorizing privacy. Originality/value This situation that others’ information breached one’s own privacy calls for an updated understanding of not only privacy but also the relationship between the person and their information. Privacy scholars need to renew their ethical language and vocabularies to properly understand the issue, which recent privacy conceptualizations are already doing (e.g. group privacy). (shrink)

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of (...) individuals correlate in practice with different modes of human–computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia. (shrink)