Explore the vast range of titles available.

There is little consensus on what artificial intelligence (AI) systems may or may not embrace. Although this may point to multiplicity of interpretations and backgrounds, a lack of conceptual clarity could thwart the development of common ground around the concept among researchers, practitioners and users of AI and pave the way for misinterpretation and abuse of the concept. This article argues that one of the effective ways to delineate the concept of AI is to compare and contrast it with human intelligence. In doing so, the article broaches the unique capabilities of humans and AI in relation to one another (human and machine tacit knowledge), as well as two types of AI systems: one that goes beyond human intelligence and one that is necessarily and inherently tied to it. It finally highlights how humans and AI can augment their capabilities and intelligence through synergistic human–AI interactions (i.e., human-augmented AI and augmented human intelligence), resulting in hybrid intelligence, and concludes with a future-looking research agenda.

Blood formation is believed to occur through stepwise progression of haematopoietic stem cells (HSCs) following a tree-like hierarchy of oligo-, bi- and unipotent progenitors. However, this model is based on the analysis of predefined flow-sorted cell populations. Here we integrated flow cytometric, transcriptomic and functional data at single-cell resolution to quantitatively map early differentiation of human HSCs towards lineage commitment. During homeostasis, individual HSCs gradually acquire lineage biases along multiple directions without passing through discrete hierarchically organized progenitor populations. Instead, unilineage-restricted cells emerge directly from a ‘continuum of low-primed undifferentiated haematopoietic stem and progenitor cells’ (CLOUD-HSPCs). Distinct gene expression modules operate in a combinatorial manner to control stemness, early lineage priming and the subsequent progression into all major branches of haematopoiesis. These data reveal a continuous landscape of human steady-state haematopoiesis downstream of HSCs and provide a basis for the understanding of haematopoietic malignancies. Velten et al.  use single-cell transcriptomics and functional data to map the early lineage commitment of human haematopoietic stem cells as a continuous process of cells passing through transitory states rather than demarcating discrete progenitors.

Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking.

Cancer stem cells drive disease progression and relapse in many types of cancer. Despite this, a thorough characterization of these cells remains elusive and with it the ability to eradicate cancer at its source. In acute myeloid leukemia (AML), leukemic stem cells (LSCs) underlie mortality but are difficult to isolate due to their low abundance and high similarity to healthy hematopoietic stem cells (HSCs). Here, we demonstrate that LSCs, HSCs, and pre-leukemic stem cells can be identified and molecularly profiled by combining single-cell transcriptomics with lineage tracing using both nuclear and mitochondrial somatic variants. While mutational status discriminates between healthy and cancerous cells, gene expression distinguishes stem cells and progenitor cell populations. Our approach enables the identification of LSC-specific gene expression programs and the characterization of differentiation blocks induced by leukemic mutations. Taken together, we demonstrate the power of single-cell multi-omic approaches in characterizing cancer stem cells. Leukaemic stem cells drive acute myeloid leukaemia (AML) progression and relapse but they are incompletely characterized. Here, the authors combine single-cell transcriptomics and clonal tracking using nuclear and mitochondrial somatic variants to distinguish healthy, pre-leukaemic and leukaemic stem cells in AML.

Conceptual research on robots and privacy has increased but we lack empirical evidence about the prevalence, antecedents, and outcomes of different privacy concerns about social robots. To fill this gap, we present a survey, testing a variety of antecedents from trust, technology adoption, and robotics scholarship. Respondents are most concerned about data protection on the manufacturer side, followed by social privacy concerns and physical concerns. Using structural equation modeling, we find a privacy paradox, where the perceived benefits of social robots override privacy concerns.

Research on digital divides has been helpful in advancing our understanding of the social structuration of Internet access, motivations to go online, digital skills, and Internet (non-)use, including participatory uses. However, digital divide research has been criticized for oversimplifying the relationship between demographic characteristics and Internet use and for its under-theorization. A social milieu approach, inspired by Pierre Bourdieu’s sociological theory, presents an excellent set of concepts to address these criticisms and thus advance digital divide research. This article uses the social milieu approach for an empirical investigation of the participation divides in Germany. Focus groups and online communities with 96 participants from seven distinct Internet milieus serve to differentiate online participation along social lines. The results show that German citizens are strongly segregated into distinct Internet milieus that differ in their intensity, variety, understanding, and attitudes toward online participation. Each milieu displays specific participatory patterns and some of the findings challenge existing research on digital and participation divides. Implications are derived and limitations of the approach carved out.

The widespread diffusion of location-based real-time dating or mobile dating apps, such as Tinder and Grindr, is changing dating practices. The affordances of these dating apps differ from those of “old school” dating sites, for example, by privileging picture-based selection, minimizing room for textual self-description, and drawing upon existing Facebook profile data. They might also affect users’ privacy perceptions as these services are location based and often include personal conversations and data. Based on a survey collected via Mechanical Turk, we assess how Tinder users perceive privacy concerns. We find that the users are more concerned about institutional privacy than social privacy. Moreover, different motivations for using Tinder—hooking up, relationship, friendship, travel, self-validation, and entertainment—affect social privacy concerns more strongly than institutional concerns. Finally, loneliness significantly increases users’ social and institutional privacy concerns, while narcissism decreases them.

A growing body of research highlights a trend toward widespread attitudes of privacy cynicism, apathy and resignation among Internet users. In this work, we extend these discussions by concentrating on the concept of user agency. Specifically, we examine how five types of structural constraints—interpersonal, cultural, technological, economic and political—restrict user agency and contribute to the prevalence of privacy cynicism as a common response. Drawing on critical data studies and adopting an intersectional lens, we demonstrate how these constraints disproportionately impact various social groups unequally, leading to a disparate distribution of agency and privacy cynicism. Furthermore, we contend that the sense of powerlessness engendered by excessive constraints on user agency can, in turn, exacerbate user vulnerability to such constraints, potentially initiating a vicious cycle of disempowerment. The article enriches the field of privacy research by linking the traditionally individual-focused and psychological dimensions of privacy with critical surveillance studies and by proposing potential interventions to mitigate privacy cynicism.

Parental sharing of child-related content on social network sites, termed “sharenting,” is often the target of criticism. Yet, through sharenting, parents can find support systems, a way to stay in touch with relevant others, and even an opportunity for additional income. This study contributes to knowledge on antecedents of sharenting. It explores the impact of parents’ privacy concerns on the sharing of child-related content, as well as on their general Instagram sharing. In this study, we differentiate between general and situational privacy. Moreover, we investigate whether parents’ privacy self-efficacy and the support of their peers influence parental sharing practices. Drawing on a rich body of literature on privacy and information sharing, we discuss the results of an online survey distributed among 320 Instagram users who are parents of children younger than 13 and reside in the United Kingdom. We find that parents’ privacy concerns are uncorrelated to sharenting and only situational concerns marginally correlate to parents’ general sharing. Parents’ reported privacy self-efficacy also did not play a role in parents’ sharing of either personal or children-related content. On the contrary, both Instagram sharing and having a network supportive of parental sharenting positively predict sharenting. Our results indicate that (a) neither situational nor general privacy concerns influence parents’ sharenting behavior, and (b) a parent’s supportive network and frequent sharing habits make frequent sharenting more likely.

The global Covid-19 pandemic has resulted in social and economic disruption unprecedented in the modern era. Many countries have introduced severe measures to contain the virus, including travel restrictions, public event bans, non-essential business closures and remote work policies. While digital technologies help governments and organizations to enforce protection measures, such as contact tracing, their rushed deployment and adoption also raises profound concerns about surveillance, privacy and data protection. This article presents two critical cases on digital surveillance technologies implemented during the Covid-19 pandemic and delineates the privacy implications thereof. We explain the contextual nature of privacy trade-offs during a pandemic and explore how regulatory and technical responses are needed to protect privacy in such circumstances. By providing a multi-disciplinary conversation on the value of privacy and data protection during a global pandemic, this article reflects on the implications digital solutions have for the future and raises the question of whether there is a way to have expedited privacy assessments that could anticipate and help mitigate adverse privacy implications these may have on society.