Religious bias landscape in language and text-to-image models
Analysis, detection, and debiasing strategies
Bibliographic Data
| ID | 20396996 |
|---|---|
| Authors | Ajwad Abrar (0009-0003-6729-9342, Islamic University of Technology, corresponding author), Nafisa Tabassum Oeshy (Islamic University of Technology), Mohsinul Kabir (0000-0001-5899-6355, National Centre for Atmospheric Science), Sophia Ananiadou (0000-0002-4097-9191, National Centre for Atmospheric Science) |
| Year | 2026 |
| Volume | 41 |
| Issue | 4 |
| Pages | 2915-2941 |
| Publication date | 2026-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | AI & Society (JOURNAL) |
| Journal identifiers | ISSN: 0951-5666 • E-ISSN: 1435-5655 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00146-025-02721-z |
| OpenAlex | W4415878358 |
| Language | EN |
| Citations received | 1 |
| References cited | 34 |
Cognitive bias · Confirmation bias · Debiasing · Gender bias · Generative grammar · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Hate Speech and Cyberbullying Detection
High-Resolution Image Synthesis with Latent Diffusion Models
Persistent Anti-Muslim Bias in Large Language Models
Semantics derived automatically from language corpora contain human-like biases
The Measurement of Observer Agreement for Categorical Data
Methodology of the Pew Research Global Religious Landscape Study
Measuring nominal scale agreement among many raters
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |