{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T15:31:50Z","timestamp":1756308710921,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP190101507"],"award-info":[{"award-number":["DP190101507"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000925","name":"John Templeton Foundation","doi-asserted-by":"publisher","award":["61378"],"award-info":[{"award-number":["61378"]}],"id":[{"id":"10.13039\/100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001230","name":"Macquarie University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001230","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ethics Inf Technol"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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 &amp; 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\u00a0Soundings 41:45\u201355,\u00a02009; in: Barocas et al., SIGCIS Conference, 2017). Whereas the bulk of researchers\u2019 attention thus far has been given to stereotyping and denigration, in this study we examine \u2018exnomination\u2019, 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 \u201creligious person.\u201d 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.<\/jats:p>","DOI":"10.1007\/s10676-024-09760-y","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T09:01:57Z","timestamp":1712912517000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Now you see me, now you don\u2019t: an exploration of religious exnomination in DALL-E"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5879-8033","authenticated-orcid":false,"given":"Mark","family":"Alfano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4145-1831","authenticated-orcid":false,"given":"Ehsan","family":"Abedin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4742-2887","authenticated-orcid":false,"given":"Ritsaart","family":"Reimann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3571-9221","authenticated-orcid":false,"given":"Marinus","family":"Ferreira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0637-3436","authenticated-orcid":false,"given":"Marc","family":"Cheong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"9760_CR201","doi-asserted-by":"crossref","unstructured":"Acerbi, A., & Stubbersfield, J. M. (2023). Large language models show human-like content biases in transmission chain experiments. Proceedings of the National Academy of Sciences, 120(44), e2313790120.","DOI":"10.1073\/pnas.2313790120"},{"key":"9760_CR1","unstructured":"Barocas, S., Crawford, K., Shapiro, A., & Wallach, H. (2017). The problem with bias: Allocative versus representational harms in machine learning. SIGCIS Conference."},{"key":"9760_CR2","volume-title":"Mythologies","author":"R Barthes","year":"1972","unstructured":"Barthes, R. (1972). Mythologies. Translated by A Lavers. Farrar, Straus & Giroux."},{"key":"9760_CR3","unstructured":"Bear, A., & Knobe, J. (2015). Folk Judgments of normality: Part statistical, part evaluative. In CogSci."},{"key":"9760_CR4","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594095","author":"F Bianchi","year":"2023","unstructured":"Bianchi, F., Kalluri, P., Durmus, E., Ladhak, F., Cheng, M., Nozza, D., Hashimoto, T., Jurafsky, D., Zou, J., & Caliskan, A. (2023). Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. Proceedings of the 2023 ACM Conference on Fairness Accountability and Transparency. https:\/\/doi.org\/10.1145\/3593013.3594095","journal-title":"Proceedings of the 2023 ACM Conference on Fairness Accountability and Transparency"},{"key":"9760_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511616037","volume-title":"The grammar of society: The nature and dynamics of social norms","author":"C Bicchieri","year":"2005","unstructured":"Bicchieri, C. (2005). The grammar of society: The nature and dynamics of social norms. Cambridge University Press."},{"key":"9760_CR6","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780190622046.001.0001","author":"C Bicchieri","year":"2017","unstructured":"Bicchieri, C. (2017). Norms in the wild: How to diagnose, measure, and change social norms. Oxford University Press. https:\/\/doi.org\/10.1093\/acprof:oso\/9780190622046.001.0001","journal-title":"Oxford University Press"},{"key":"9760_CR7","unstructured":"Bouchard, L. (2022). How does dalle-mini work? Louis Bouchard. https:\/\/www.louisbouchard.ai\/dalle-mini\/"},{"key":"9760_CR8","first-page":"77","volume":"81","author":"J Buolamwini","year":"2018","unstructured":"Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness Accountability and Transparency, 81, 77\u201391.","journal-title":"Proceedings of the 1st Conference on Fairness Accountability and Transparency"},{"issue":"1","key":"9760_CR9","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/TBIOM.2020.3027269","volume":"3","author":"JG Cavazos","year":"2020","unstructured":"Cavazos, J. G., Phillips, P. J., Castillo, C. D., & O\u2019Toole, A. J. (2020). Accuracy comparison across face recognition algorithms: Where are we on measuring race bias? IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(1), 101\u2013111.","journal-title":"IEEE Transactions on Biometrics, Behavior, and Identity Science"},{"key":"9760_CR10","doi-asserted-by":"publisher","unstructured":"Cheong, M., Ferreira, M., Alfano, M., Reimann, R., Abedin, E., Klein, C., Chalson, S., Robinson, P., Byrne, J., & Ruppanner, L. (2024). Investigating biases in DALL-E mini images. ACM Journal on Responsible Computing (2024, March).\u00a0https:\/\/doi.org\/10.1145\/3649883","DOI":"10.1145\/3649883"},{"key":"9760_CR11","doi-asserted-by":"crossref","unstructured":"Cho, J., Zala, A., & Bansal, M. (2022). Dall-eval: Probing the reasoning skills and social biases of text-to-image generative transformers. arXiv preprint\u00a0http:\/\/arXiv.org\/2202.04053","DOI":"10.1109\/ICCV51070.2023.00283"},{"key":"9760_CR12","unstructured":"Conwell, C., & Ullman, T. (2022). Testing relational understanding in text-guided image generation. arXiv preprint http:\/\/arXiv.org\/2208.00005"},{"key":"9760_CR13","doi-asserted-by":"publisher","first-page":"45","DOI":"10.3898\/136266209787778939","volume":"41","author":"K Crawford","year":"2009","unstructured":"Crawford, K. (2009). Adult responsibility in insecure times. Soundings, 41, 45\u201355.","journal-title":"Soundings"},{"key":"9760_CR14","doi-asserted-by":"publisher","unstructured":"Dayma, B., Patil, S., Cuenca, P., Saifullah, K., Abraham, T., L\u00ea Kh\u1eafc, P., Melas, L., & Ghosh, R. (2021). DALL\u00b7E Mini. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.5146400","DOI":"10.5281\/zenodo.5146400"},{"key":"9760_CR15","unstructured":"Dayma, B., Patil, S., Cuenca, P., Saifullah, K., Abraham, T., L\u00ea Kh\u1eafc, P., Melas, L., & Ghosh, R. (2022). DALL-E Mini Explained. Weights & Biases; Weights and Biases, Inc. https:\/\/wandb.ai\/dalle-mini\/dalle-mini\/reports\/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA"},{"key":"9760_CR16","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287572","author":"M De-Arteaga","year":"2019","unstructured":"De-Arteaga, M., Romanov, A., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S., Kenthapadi, K., & Kalai, A. T. (2019). Bias in bios: A case study of semantic representation bias in a high-stakes setting. Proceedings of the Conference on Fairness, Accountability, and Transparency. https:\/\/doi.org\/10.1145\/3287560.3287572","journal-title":"Proceedings of the Conference on Fairness, Accountability, and Transparency"},{"key":"9760_CR17","unstructured":"Deery, O and Bailey, K (2018). Ethics, bias and statistical models. Input paper for the Horizon Scanning Project \u201cThe Effective and Ethical Development of Artificial Intelligence: An Opportunity to Improve Our Wellbeing\u201d on behalf of the Australian Council of Learned Academies, https:\/\/www.acola.org"},{"key":"9760_CR18","doi-asserted-by":"publisher","DOI":"10.4324\/9781315544786","volume-title":"White","author":"R Dyer","year":"2017","unstructured":"Dyer, R. (2017). White. Routledge."},{"key":"9760_CR19","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/978-1-4612-0473-2_42","volume-title":"Nuclear Analytical Methods in the Life Sciences","author":"KJ Ellis","year":"1990","unstructured":"Ellis, K. J. (1990). Reference man and woman more fully characterized. In R. Zeisler & V. Guinn (Eds.), Nuclear Analytical Methods in the Life Sciences (pp. 385\u2013400). Humana Press."},{"key":"9760_CR20","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., Kuprel, B., Novoa, R., Ko, J., Swetter, S., Blau, H., & Thrun, s. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115\u2013118.","journal-title":"Nature"},{"key":"9760_CR21","doi-asserted-by":"publisher","first-page":"560","DOI":"10.2307\/2669264","volume":"44","author":"FD Gilliam Jr","year":"2000","unstructured":"Gilliam, F. D., Jr., & Iyengar, S. (2000). Prime suspects: The influence of local television news on the viewing public. American Journal of Political Science, 44, 560\u2013573.","journal-title":"American Journal of Political Science"},{"key":"9760_CR200","doi-asserted-by":"crossref","unstructured":"Ghosh, S., & Caliskan, A. (2023). Chatgpt perpetuates gender bias in machine translation and ignores non-gendered pronouns: Findings across bengali and five other low-resource languages. In Proceedings of the 2023 AAAI\/ACM Conference on AI, Ethics, and Society. pp. 901\u2013912.","DOI":"10.1145\/3600211.3604672"},{"key":"9760_CR22","unstructured":"Hackett, C. & Mcclendon, D. (2015). Christians remain world\u2019s largest religious group, but they are declining in Europe. Pew research center.\u00a0Retrieved\u00a0October 8, 2022, from\u00a0https:\/\/www.pewresearch.org\/fact-tank\/2017\/04\/05\/christians-remain-worlds-largest-religious-group-but-they-are-declining-in-europe\/"},{"issue":"2","key":"9760_CR23","first-page":"29","volume":"29","author":"J Haidt","year":"2016","unstructured":"Haidt, J., & Jussim, L. (2016). Psychological science and viewpoint diversity. APS Observer, 29(2), 29.","journal-title":"APS Observer"},{"issue":"7302","key":"9760_CR24","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1038\/466029a","volume":"466","author":"J Henrich","year":"2010","unstructured":"Henrich, J., Heine, S. J., & Norenzayan, A. (2010). Most people are not WEIRD. Nature, 466(7302), 29\u201329.","journal-title":"Nature"},{"issue":"3","key":"9760_CR25","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1111\/hypa.12483","volume":"34","author":"K Hutchison","year":"2019","unstructured":"Hutchison, K. (2019). Gender bias in medical implant design and use: A type of moral aggregation problem? Hypatia, 34(3), 570\u2013591.","journal-title":"Hypatia"},{"key":"9760_CR26","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.cognition.2017.01.010","volume":"161","author":"TF Icard","year":"2017","unstructured":"Icard, T. F., Kominsky, J. F., & Knobe, J. (2017). Normality and actual causal strength. Cognition, 161, 80\u201393.","journal-title":"Cognition"},{"key":"9760_CR27","first-page":"293","volume":"22","author":"R Langton","year":"1993","unstructured":"Langton, R. (1993). Speech acts and unspeakable acts. Philosophy and Public Affairs, 22, 293\u2013330.","journal-title":"Philosophy and Public Affairs"},{"key":"9760_CR28","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199247066.001.0001","volume-title":"Sexual solipsism: Philosophical essays on pornography and objectification","author":"R Langton","year":"2009","unstructured":"Langton, R. (2009). Sexual solipsism: Philosophical essays on pornography and objectification. Oxford University Press."},{"key":"9760_CR29","first-page":"72","volume-title":"Speech and harm: Controversies over free speech","author":"R Langton","year":"2012","unstructured":"Langton, R. (2012). Beyond belief: Pragmatics in hate speech and pornography. In I. Maitra & M. K. McGowan (Eds.), Speech and harm: Controversies over free speech (pp. 72\u201393). Oxford university press."},{"key":"9760_CR30","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In arXiv [cs.CL]. arXiv. http:\/\/arxiv.org\/abs\/1910.13461","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"9760_CR31","doi-asserted-by":"crossref","unstructured":"Lohr, S. (2022). Facial recognition is accurate, if you're a white guy. In Ethics of Data and Analytics (pp. 143\u2013147). Auerbach Publications.","DOI":"10.1201\/9781003278290-22"},{"key":"9760_CR32","unstructured":"Luccioni, A. S., Akiki, C., Mitchell, M., & Jernite, Y. (2023). Stable Bias: Analyzing Societal Representations in Diffusion Models"},{"key":"9760_CR33","volume-title":"Race, Class, and Gender: An Anthology","author":"P McIntosh","year":"1992","unstructured":"McIntosh, P. (1992). White privilege and male privilege: A personal account of coming to see correspondences through work in women\u2019s studies. In M. Anderson & P. H. Collins (Eds.), Race, Class, and Gender: An Anthology. Wadsworth."},{"key":"9760_CR34","doi-asserted-by":"publisher","unstructured":"Milli\u00e8re, R. (2022). Adversarial attacks on image generation with made-up words.\u00a0Retrieved\u00a0October 15, 2022, from\u00a0\u00a0https:\/\/doi.org\/10.48550\/arXiv.2208.04135","DOI":"10.48550\/arXiv.2208.04135"},{"key":"9760_CR35","volume-title":"The Anti-Christ, Ecce Homo, Twilight of the Idols, and Other Writings","author":"F Nietzsche","year":"2005","unstructured":"Nietzsche, F. (2005). The Anti-Christ, Ecce Homo, Twilight of the Idols, and Other Writings. Cambridge University Press."},{"key":"9760_CR36","unstructured":"Offert, F., & Phan, T. (2022). A Sign That Spells: DALL-E 2, Invisual Images and The Racial Politics of Feature Space http:\/\/arxiv.org\/2211.06323"},{"key":"9760_CR37","unstructured":"OpenAI (2022), Reducing bias and improving safety in DALL\u00b7E 2. (2022). Retrieved January 18,\u00a0 2024, from https:\/\/openai.com\/blog\/reducing-bias-and-improving-safety-in-dall-e-2"},{"key":"9760_CR38","unstructured":"Peterson, J. B. (2018). 12 Rules for Life: An Antidote to Chaos. Penguin UK."},{"key":"9760_CR39","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3616679","author":"Y Qu","year":"2023","unstructured":"Qu, Y., Shen, X., He, X., Backes, M., Zannettou, S., & Zhang, Y. (2023). Unsafe diffusion: On the generation of unsafe images and hateful memes from text-to-image models. Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. https:\/\/doi.org\/10.1145\/3576915.3616679","journal-title":"Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security"},{"key":"9760_CR40","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375820","author":"ID Raji","year":"2020","unstructured":"Raji, I. D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., & Denton, E. (2020). Saving face: Investigating the ethical concerns of facial recognition auditing. Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society. https:\/\/doi.org\/10.1145\/3375627.3375820","journal-title":"Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society"},{"key":"9760_CR41","doi-asserted-by":"crossref","unstructured":"Schramowski, P., Brack, M., Deiseroth, B., & Kersting, K. (2023). Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 22522\u201322531).","DOI":"10.1109\/CVPR52729.2023.02157"},{"issue":"4","key":"9760_CR42","first-page":"169","volume":"82","author":"A Sen","year":"1985","unstructured":"Sen, A. (1985). Well-being, agency and freedom: The dewey lectures 1984. The Journal of Philosophy, 82(4), 169\u2013221.","journal-title":"The Journal of Philosophy"},{"key":"9760_CR43","unstructured":"Seshadri, P., Singh, S., & Elazar, Y. (2023). The Bias Amplification Paradox in Text-to-Image Generation http:\/\/arxiv.org\/2308.00755"},{"issue":"3397","key":"9760_CR44","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/S0262-4079(22)01329-X","volume":"255","author":"M Sparkes","year":"2022","unstructured":"Sparkes, M. (2022). AI art tool covertly alters requests. New Scientist, 255(3397), 10. https:\/\/doi.org\/10.1016\/S0262-4079(22)01329-X","journal-title":"New Scientist"},{"key":"9760_CR45","unstructured":"Wiggers, K. (2022, October 12). Microsoft brings DALL-E 2 to the masses with Designer and Image Creator. TechCrunch. https:\/\/techcrunch.com\/2022\/10\/12\/microsoft-brings-dall-e-2-to-the-masses-with-designer-and-image-creator\/."},{"key":"9760_CR46","unstructured":"Wu, Y., Yu, N., Backes, M., Shen, Y., & Zhang, Y. (2023). On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts http:\/\/arxiv.org\/2310.16613"},{"key":"9760_CR47","first-page":"2979","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","author":"J Zhao","year":"2017","unstructured":"Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K. W. (2017). Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In M. Palmer, R. Hwa, & S. Riedel (Eds.), Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 2979\u20132989). Association for Computational Linguistics."}],"container-title":["Ethics and Information Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10676-024-09760-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10676-024-09760-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10676-024-09760-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T13:20:41Z","timestamp":1718889641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10676-024-09760-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":49,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9760"],"URL":"https:\/\/doi.org\/10.1007\/s10676-024-09760-y","relation":{},"ISSN":["1388-1957","1572-8439"],"issn-type":[{"type":"print","value":"1388-1957"},{"type":"electronic","value":"1572-8439"}],"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"9 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"27"}}