If I decide to disclose information about myself, this act may undermine other people’s ability to conceal information about them. Such dependencies are called privacy dependencies in the literature. Some say that privacy dependencies generate moral duties to avoid sharing information about oneself. If true, we argue, then it is sometimes justified for others to impose harm on the person sharing information to prevent them from doing so. In this paper, we first show how such conclusions arise. Next, we show (...) that the existence of such a dependency between the moral significance you are inclined to attribute to privacy dependencies and judgments about permissible self-defense puts pressure on at least some ways of spelling out the idea that privacy dependencies ought to constrain our data-sharing conduct. (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)

AI-enabled predictive analytics is widely deployed in clinical care settings for healthcare monitoring, diagnostics and risk management. The technology may offer valuable insights into individual and population health patterns, trends and outcomes. Predictive analytics may, however, also tangibly affect individual patient privacy and the right thereto. On the one hand, predictive analytics may undermine a patient’s state of privacy by constructing or modifying their health identity independent of the patient themselves. On the other hand, the use of predictive analytics may (...) violate the patient’s right to privacy if the patient has no control over the use or output of the technology. These repercussions ultimately erode patient autonomy and agency. This paper discusses these implications in further detail, and proposes possible measures for their mitigation. They involve the incorporation in the AI systems of accuracy-enhancing statistical models and methods, more privacy-conscious institutional policies and practices, and effective choice for patients to accept or refuse diagnostics and treatment drawing on AI-enabled predictive analytics. (shrink)

The proliferation of AI systems across all domains of life as well as the complexification and opacity of algorithmic techniques, epitomised by the bourgeoning field of Deep Learning (DL), call for new methods in the Humanities for reflecting on the techno-human relation in a way that places the technical operation at its core. Grounded on the work of the philosopher of technology Gilbert Simondon, this paper puts forward individuation theory as a valuable approach to reflect on contemporary information technologies, offering (...) an analysis of the functioning of deep neural networks (DNNs), a type of data-driven computational models at the core of major breakthroughs in AI. The purpose of this article is threefold: (1) to demonstrate how a joint reading of Simondon’s mechanology and individuation theory, foregrounded in the Simondonian concept of information, can cast new light on contemporary algorithmic techniques by considering their situated emergence as opposed to technical lineage; (2) to suspend a predictive framing of AI systems, particularly DL techniques, so as to probe into their technical operation, accounting for the data-driven individuation of these models and the integration of potentials as functionality; and finally, (3) to argue that individuation theory might in fact de-individuate AI, in the sense of disassembling the already-there, the constituted, paving the way for questioning the potentialities for data and their algorithmic relationality to articulate the unfolding of everyday life. (shrink)

Sharing research data has great potential to benefit science and society. However, data sharing is still not common practice. Since public research funding agencies have a particular impact on research and researchers, the question arises: Are public funding agencies morally obligated to promote data sharing? We argue from a research ethics perspective that public funding agencies have several pro tanto obligations requiring them to promote data sharing. However, there are also pro tanto obligations that speak against promoting data sharing in (...) general as well as with regard to particular instruments of such promotion. We examine and weigh these obligations and conclude that all things considered funders ought to promote the sharing of data. Even the instrument of mandatory data sharing policies can be justified under certain conditions. (shrink)

The rapid progress of artificial intelligence (AI) has resulted in its integration into various stages of the media process, including information gathering, processing, and distribution. This integration has raised the possibility of AI dominating the media industry, leading to an era of “autonomous driving” within AI-driven media systems. Similar to the ethical dilemma known as the “trolley problem” (TP) in autonomous driving, a comparable problem arises in AI automated media. This study examines the emergence of the new TP in fully (...) automated AI-driven media (FAAIM), recognizing the complexity of this problem given the nature of media content and its societal impact. To address this complexity, we propose the adoption of the theory of “meaningful human control,” originally developed to address responsibility in the governance of lethal autonomous weapons systems, as a framework for governing FAAIM. By ensuring that humans can be held responsible for the operations of FAAIM, this paper aims to proactively confront the ethical challenges arising from FAAIM, identify potential solutions, and guide future research in media ethics. (shrink)

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)

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)