Pope Leo XIV speaks during a meeting with bishops, members of the clergy and families whose members have been victims of environmental pollution at the Cathedral of Santa Maria Assunta, in Acerra, Italy, on 23 May 2026. Pope Leo XIV speaks during a meeting with bishops, members of the clergy and families whose members have been victims of environmental pollution at the Cathedral of Santa Maria Assunta, in Acerra, Italy, on 23 May 2026. Americans echo Pope Leo's concerns about AI: 'It threatens workers, privacy and human life' Guardian readers in the US spoke of fears about unregulated AI in response to the pope's encyclical warning about the risks of the technology I n his first major papal text since assuming leadership of the Catholic church last year, Pope Leo issued a stark warning about the rise of artificial intelligence this week, denouncing the "culture of power" driving the AI age. Calling for the "most rigorous" ethical constraints on AI - which he described as one of the greatest threats facing humanity today - the first US-born pope also warned of "new forms of slavery" emerging through the digital economy. Speaking to the Guardian, readers in the US echoed the pope's concerns, describing AI as an "unregulated" industry increasingly being used to the "detriment of too many people", while also raising fears about surveillance, labor displacement, war and environmental harm .

The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Open source libraries such as HuggingFace have made these models easily available and accessible. While prior research has identified biases in large language models, this paper considers biases contained in the most popular versions of these models when applied'out-of-the-box' for downstream tasks. We focus on generative language models as they are well-suited for extracting biases inherited from training data. Specifically, we conduct an indepth analysis of GPT-2, which is the most downloaded text generation model on HuggingFace, with over half a million downloads per month. We assess biases related to occupational associations for different protected categories by intersecting gender with religion, sexuality, ethnicity, political affiliation, and continental name origin. Using a template-based data collection pipeline, we collect 396K sentence completions made by GPT-2 and find: (i) The machine-predicted jobs are less diverse and more stereotypical for women than for men, especially for intersections; (ii) Intersectional interactions are highly relevant for occupational associations, which we quantify by fitting 262 logistic models; (iii) For most occupations, GPT-2 reflects the skewed gender and ethnicity distribution found in USLabor Bureau data, and even pulls the societally-skewed distribution towards gender parity in cases where its predictions deviate from real labor market observations. This raises the normative question of what language models should learn - whether they should reflect or correct for existing inequalities.

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While recent developments in large language models have improved bias detection and classification, sensitive subjects like religion still present challenges because even minor errors can result in severe misunderstandings. In particular, multilingual models often misrepresent religions and have difficulties being accurate in religious contexts. To address this, we introduce BRAND: Bilingual Religious Accountable Norm Dataset, which focuses on the four main religions of South Asia: Buddhism, Christianity, Hinduism, and Islam, containing over 2,400 entries, and we used three different types of prompts in both English and Bengali. Our results indicate that models perform better in English than in Bengali and consistently display bias toward Islam, even when answering religion-neutral questions. These findings highlight persistent bias in multilingual models when similar questions are asked in different languages. We further connect our findings to the broader issues in HCI regarding religion and spirituality.