Is the Bible the unembellished Word of God or the product of human agency? There are different answers to that question. And they lie at the heart of this book's powerful exploration of the fraught ways in which money, race and power shape the story of Christianity in American public life. The authors' subject is the Museum of the Bible in Washington, DC: arguably the latest example of a long line of white evangelical institutions aiming to amplify and promote a (...) religious, political, and moral agenda of their own. In their careful and compelling investigation, Jill Hicks-Keeton and Cavan Concannon disclose the ways in which the Museum's exhibits reinforce a particularized and partial interpretation of the Bible's meaning. Bringing to light the Museum's implicit messaging about scriptural provenance and audience, the authors reveal how the MOTB produces a version of the Bible that in essence authorizes a certain sort of white evangelical privilege; promotes a view of history aligned with that same evangelical aspiration; and above all protects a cohort of white evangelicals from critique. They show too how the Museum collapses vital conceptual distinctions between its own conservative vision of the Bible and 'The Bible' as a cultural icon. This revelatory volume above all confirms that scripture – for all the claims made for it that it speaks only divine truth – can in the end never be separated from human politics. (shrink)

Dialogue systems, from Virtual Personal Assistants such as Siri, Cortana, and Alexa to state-of-the-art systems such as BlenderBot3 and ChatGPT, are already widely available, used in a variety of applications, and are increasingly part of many people’s lives. However, the task of enabling them to use empathetic language more convincingly is still an emerging research topic. Such systems generally make use of complex neural networks to learn the patterns of typical human language use, and the interactions in which the systems (...) participate are usually mediated either via interactive text-based or speech-based interfaces. In human–human interaction, empathy has been shown to promote prosocial behaviour and improve interaction. In the context of dialogue systems, to advance the understanding of how perceptions of empathy affect interactions, it is necessary to bring greater clarity to how empathy is measured and assessed. Assessing the way dialogue systems create perceptions of empathy brings together a range of technological, psychological, and ethical considerations that merit greater scrutiny than they have received so far. However, there is currently no widely accepted evaluation method for determining the degree of empathy that any given system possesses (or, at least, appears to possess). Currently, different research teams use a variety of automated metrics, alongside different forms of subjective human assessment such as questionnaires, self-assessment measures and narrative engagement scales. This diversity of evaluation practice means that, given two DSs, it is usually impossible to determine which of them conveys the greater degree of empathy in its dialogic exchanges with human users. Acknowledging this problem, the present article provides an overview of how empathy is measured in human–human interactions and considers some of the ways it is currently measured in human–DS interactions. Finally, it introduces a novel third-person analytical framework, called the Empathy Scale for Human–Computer Communication (ESHCC), to support greater uniformity in how perceived empathy is measured during interactions with state-of-the-art DSs. (shrink)

This article probes the practical ethical implications of AI system design by reconsidering the important topic of bias in the datasets used to train autonomous intelligent systems. The discussion draws on recent work concerning behaviour-guiding technologies, and it adopts a cautious form of technological utopianism by assuming it is potentially beneficial for society at large if AI systems are designed to be comparatively free from the biases that characterise human behaviour. However, the argument presented here critiques the common well-intentioned requirement (...) that, in order to achieve this, all such datasets must be debiased prior to training. By focusing specifically on gender-bias in Neural Machine Translation systems, three automated strategies for the removal of bias are considered – downsampling, upsampling, and counterfactual augmentation – and it is shown that systems trained on datasets debiased using these approaches all achieve general translation performance that is much worse than a baseline system. In addition, most of them also achieve worse performance in relation to metrics that quantify the degree of gender bias in the system outputs. By contrast, it is shown that the technique of domain adaptation can be effectively deployed to debias existing NMT systems after they have been fully trained. This enables them to produce translations that are quantitatively far less biased when analysed using gender-based metrics, but which also achieve state-of-the-art general performance. It is hoped that the discussion presented here will reinvigorate ongoing debates about how and why bias can be most effectively reduced in state-of-the-art AI systems. (shrink)

While for decades nursing has advocated for theory‐informed practice, more recent attention has tended to focus on mid‐range theory rather than the earlier focus on developing grand theory to encompass all of nursing practice. However, there has been continued interest in the holistic nursing community on grand theory and, in particular, on Integral Theory. Although Integral Theory's four‐quadrant (AQAL) perspective is familiar in nursing, little is known about how it is being used by nurses in direct practice. The purpose of (...) this interpretive descriptive study was to provide a practice‐based perspective on Ken Wilber's Integral Theory in professional nursing practice. The following research question was investigated: How does Integral Theory assist nurses in describing and understanding their professional work? Nurses participating in this study used Integral Theory as a map or heuristic that gave structure to an inquiry process in professional nursing practice and in nursing education in a manner that was holonic, multiperspectival, and self‐reflective. Challenges constraining nurses’ use of Integral Theory included its intricacy, as well as contextual factors in practice environments. Implications for nursing practice and education for the use of Integral Theory's meta‐framework are described. (shrink)

Abstract:This study analyses how the migrant experience is reflected in the geography of Hugo Hamilton's work of fiction entitled Hand in the Fire (2010). The novel is told from the point of view of Vid, a Serbian immigrant who is trying to settle in Ireland. The young carpenter endeavours to fit in Irish society through his friendship with a young Dublin lawyer, Kevin Concannon, who tells him that a true friend would put his “hand in the fire” for you. (...) Through the portrayal of these displaced characters, Hamilton explores his favourite themes of memory and belonging. This article focuses on the places depicted in the novel, from the Serbia that Vid has left in his past to the modern-day Dublin and the west coast of Ireland, in order to analyse how the settings of the narrative reflect the characters’ state of mind. Using Deleuze and Guattari's concepts of de/re-territorialization and of striated/smooth spaces, the examination of the geographies of the novel allows for a better understanding of the issues at stake for the characters. The liminal, deterritorialized figure of the immigrant represented by Vid is echoed by the smooth islands of the novel. In an archipelagic country where the coastline stands for the visual border, it is no wonder that the transnational protagonist is drawn to the Irish shores. On the edge of two cultures, Vid, who lives in Dún Laoghaire, south of Dublin, is gradually moving west to the striated landscape of Connemara, where he investigates on Kevin's aunt who drowned and was washed ashore the Aran Islands. (shrink)