Now you see me, now you don't: an exploration of religious exnomination in DALL-E.

Artificial intelligence (AI) systems are increasingly being used not only to classify and analyze but also to generate images and text. As recent work on the content produced by text and image Generative AIs has shown (e.g., Cheong et al., 2024, Acerbi & Stubbersfield, 2023), there is a risk that harms of representation and bias, already documented in prior AI and natural language processing (NLP) algorithms may also be present in generative models. These harms relate to protected categories such as gender, race, age, and religion. There are several kinds of harms of representation to consider in this context, including stereotyping, lack of recognition, denigration, under-representation, and many others (Crawford in Soundings 41:45-55, 2009; in: Barocas et al., SIGCIS Conference, 2017). Whereas the bulk of researchers' attention thus far has been given to stereotyping and denigration, in this study we examine 'exnomination', as conceived by Roland Barthes (1972), of religious groups. Our case study is DALL-E, a tool that generates images from natural language prompts. Using DALL-E mini, we generate images from generic prompts such as "religious person." We then examine whether the generated images are recognizably members of a nominated group. Thus, we assess whether the generated images normalize some religions while neglecting others. We hypothesize that Christianity will be recognizably represented more frequently than other religious groups. Our results partially support this hypothesis but introduce further complexities, which we then explore.

Moral universals: A machine-reading analysis of 256 societies.

What is the cross-cultural prevalence of the seven moral values posited by the theory of "morality-as-cooperation"? Previous research, using laborious hand-coding of ethnographic accounts of ethics from 60 societies, found examples of most of the seven morals in most societies, and observed these morals with equal frequency across cultural regions. Here we replicate and extend this analysis by developing a new Morality-as-Cooperation Dictionary (MAC-D) and using Linguistic Inquiry and Word Count (LIWC) to machine-code ethnographic accounts of morality from an additional 196 societies (the entire Human Relations Area Files, or HRAF, corpus). Again, we find evidence of most of the seven morals in most societies, across all cultural regions. The new method allows us to detect minor variations in morals across region and subsistence strategy. And we successfully validate the new machine-coding against the previous hand-coding. In light of these findings, MAC-D emerges as a theoretically-motivated, comprehensive, and validated tool for machine-reading moral corpora. We conclude by discussing the limitations of the current study, as well as prospects for future research.

Exploring intellectual humility through the lens of artificial intelligence: Top terms, features and a predictive model.

Intellectual humility (IH) is often conceived as the recognition of, and appropriate response to, your own intellectual limitations. As far as we are aware, only a handful of studies look at interventions to increase IH - e.g. through journalling - and no study so far explores the extent to which having high or low IH can be predicted. This paper uses machine learning and natural language processing techniques to develop a predictive model for IH and identify top terms and features that indicate degrees of IH. We trained our classifier on the dataset from an existing psychological study on IH, where participants were asked to journal their experiences with handling social conflicts over 30 days. We used Logistic Regression (LR) to train a classifier and the Linguistic Inquiry and Word Count (LIWC) dictionaries for feature selection, picking out a range of word categories relevant to interpersonal relationships. Our results show that people who differ on IH do in fact systematically express themselves in different ways, including through expression of emotions (i.e., positive, negative, and specifically anger, anxiety, sadness, as well as the use of swear words), use of pronouns (i.e., first person, second person, and third person) and time orientation (i.e., past, present, and future tenses). We discuss the importance of these findings for IH and the value of using such techniques for similar psychological studies, as well as some ethical concerns and limitations with the use of such semi-automated classifications.

To chat or bot to chat: Ethical issues with using chatbots in mental health.

This paper presents a critical review of key ethical issues raised by the emergence of mental health chatbots. Chatbots use varying degrees of artificial intelligence and are increasingly deployed in many different domains including mental health. The technology may sometimes be beneficial, such as when it promotes access to mental health information and services. Yet, chatbots raise a variety of ethical concerns that are often magnified in people experiencing mental ill-health. These ethical challenges need to be appreciated and addressed throughout the technology pipeline. After identifying and examining four important ethical issues by means of a recognised ethical framework comprised of five key principles, the paper offers recommendations to guide chatbot designers, purveyers, researchers and mental health practitioners in the ethical creation and deployment of chatbots for mental health.

"Tell us what's going on": Exploring the information needs of pregnant and post-partum women in Australia during the pandemic with 'Tweets', 'Threads', and women's views.

INTRODUCTION: The provision of maternity services in Australia has been significantly disrupted in response to the COVID-19 pandemic. Many changes were initiated quickly, often with rapid dissemination of information to women. The aim of this study was to better understand what information and messages were circulating regarding COVID-19 and pregnancy in Australia and potential information gaps. METHODS: This study adopted a qualitative approach using social media and interviews. A data analytics tool (TIGER-C19) was used to extract data from social media platforms Reddit and Twitter from June to July 2021 (in the middle of the third COVID-19 wave in Australia). A total of 21 individual semi-structured interviews were conducted with those who were, or had been, pregnant in Australia since March 2020. Social media data were analysis via inductive content analysis and interview data were thematically analysed. RESULTS: Social media provided a critical platform for sharing and seeking information, as well as highlighting attitudes of the community towards COVID-19 vaccines in pregnancy. Women interviewed described wanting further information on the risks COVID-19 posed to themselves and their babies, and greater familiarity with the health service during pregnancy, in which they would labour and give birth. Health providers were a trusted source of information. Communication strategies that allowed participants to engage in real-time interactive discussions were preferred. A real or perceived lack of information led participants to turn to informal sources, increasing the potential for exposure to misinformation. CONCLUSION: It is vital that health services communicate effectively with pregnant women, early and often throughout public health crises, such as the COVID-19 pandemic. This was particularly important during periods of increased restrictions on accessing hospital services. Information and communication strategies need to be clear, consistent, timely and accessible to reduce reliance on informal and potentially inaccurate sources.

Polarization and trust in the evolution of vaccine discourse on Twitter during COVID-19.

Trust in vaccination is eroding, and attitudes about vaccination have become more polarized. This is an observational study of Twitter analyzing the impact that COVID-19 had on vaccine discourse. We identify the actors, the language they use, how their language changed, and what can explain this change. First, we find that authors cluster into several large, interpretable groups, and that the discourse was greatly affected by American partisan politics. Over the course of our study, both Republicans and Democrats entered the vaccine conversation in large numbers, forming coalitions with Antivaxxers and public health organizations, respectively. After the pandemic was officially declared, the interactions between these groups increased. Second, we show that the moral and non-moral language used by the various communities converged in interesting and informative ways. Finally, vector autoregression analysis indicates that differential responses to public health measures are likely part of what drove this convergence. Taken together, our results suggest that polarization around vaccination discourse in the context of COVID-19 was ultimately driven by a trust-first dynamic of political engagement.

Attention and counter-framing in the Black Lives Matter movement on Twitter.

The social media platform Twitter platform has played a crucial role in the Black Lives Matter (BLM) movement. The immediate, flexible nature of tweets plays a crucial role both in spreading information about the movement's aims and in organizing individual protests. Twitter has also played an important role in the right-wing reaction to BLM, providing a means to reframe and recontextualize activists' claims in a more sinister light. The ability to bring about social change depends on the balance of these two forces, and in particular which side can capture and maintain sustained attention. The present study examines 2 years worth of tweets about BLM (about 118 million in total). Timeseries analysis reveals that activists are better at mobilizing rapid attention, whereas right-wing accounts show a pattern of moderate but more sustained activity driven by reaction to political opponents. Topic modeling reveals differences in how different political groups talk about BLM. Most notably, the murder of George Floyd appears to have solidified a right-wing counter-framing of protests as arising from dangerous "terrorist" actors. The study thus sheds light on the complex network and rhetorical effects that drive the struggle for online attention to the BLM movement.