[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118312-en":3,"doc-seo-118312-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118312,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning, Synthetic Data, and the Politics of Difference","Machine learning systems are often framed through a politics of sameness, where training, testing, and deployment displace heterogeneity in favor of socially proximate similarities. In contrast, this paper develops a heterophilic logic at the intersection of synthetic data and machine learning: actively generating differences and heterogeneous attributes to train, fine-tune, and optimize algorithms. Even so, synthetic attributes remain machine-compatible while stripping away socio-cultural dynamics and conflicts. A critical analysis of disentanglement, compositionality, and normativity argues that this approach can challenge interventions by foregrounding machine-learning’s systemic unfairness and violence.","This is a repository copy of Machine Learning, Synthetic Data, and the Politics of Difference.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/222157/](https://eprints.whiterose.ac.uk/222157/)  \nVersion: Published Version  \nArticle:  \nJacobsen, Benjamin [orcid.org/0000-0002-6656-8892](orcid.org/0000-0002-6656-8892) (2025) Machine Learning, Synthetic Data, and the Politics of Difference. Theory, Culture and Society. ISSN 0263-2764  \n[https://doi.org/10.1177/02632764241304687](https://doi.org/10.1177/02632764241304687)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nArticle    \nMachine Learning, Synthetic Data, and the Politics of Difference  \nTheory, Culture & Society 1–17  \n© The Author(s) 2025  \nArticle reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/02632764241304687](DOI: 10.1177/02632764241304687)[ ](DOI: 10.1177/02632764241304687)[journals.sagepub.com/home/tcs](journals.sagepub.com/home/tcs)  \nBenjamin N. Jacobsen  \nUniversity of York  \nAbstract  \nWhat is the relationship between ideas of sameness and difference for machine learning and AI? Algorithms are often understood to participate in the continual displacement of the different and heterogeneous in society in favour of sameness, of that which is socio-politically similar and proximate. In contrast to this prevalent emphasis on sameness, however, this paper argues that there is a nascent heterophilic logic underpinning the intersection of synthetic data and machine learning, a move towards actively generating differences and heterogeneous data attributes to train, fine-tune, and optimize algorithms. Yet, these synthetic attribute data are nonetheless always machine compatible, devoid of their socio-cultural dynamics and tensions. As such, through a critical examination of three core dimensions of this emergent politics of difference of synthetic data – disentanglement, compositionality, and normativity – the paper argues that this has the potential to ultimately undercut a politics of intervention that seeks to foreground the systemic unfairness and violence of machine learning.  \nKeywords  \nalgorithms, bias, data, difference, fairness, machine learning, synthetic data  \nIntroduction  \nIn a 2018 interview, CEO of tech company Affectiva and a pioneer of so-called ‘Emotional AI’, Rana el Kaliouby, was asked about the relationship between bias and AI. She responded that ‘It’s the data. It’s how we’re applying this data’, which means that ‘we need to make sure that the training data is representative of all the different ethnic groups, and that it has gender balance and age balance’(Ford, 2018: 222) . Unsurprisingly, issues of bias and representativeness in machine learning and its training data have only become more pressing for computer scientists and researchers since Kaliouby’s interview in 2018  \nCorresponding author: Benjamin N. Jacobsen. Email: [benjamin.jacobsen@york.ac.uk](benjamin.jacobsen@york.ac.uk)  \nTCS Online Forum: [https://www.theoryculturesociety.org/](https://www.theoryculturesociety.org/)  \n(e.g. Barocas et al., 2023; Wang et al., 2022). Yet, a recurrent response to such issues has been to b","cbCaipcOZSbkeZJ4","https://ap.wps.com/l/cbCaipcOZSbkeZJ4","pdf",177231,1,18,"English","en",105,"# Introduction\n## Bias, representativeness, and the drive toward sameness\n## A heterophilic logic: generating differences with synthetic data","[{\"question\":\"这篇论文研究的核心问题是什么？\",\"answer\":\"论文探讨机器学习与AI中“相同与差异”的思想之间的关系，并说明合成数据与机器学习如何形成一种新的差异生成逻辑。\"},{\"question\":\"论文如何概括合成数据与机器学习的“异质性/异向性（heterophilic）”逻辑？\",\"answer\":\"论文认为研究者倾向于主动生成差异与异质属性，用于训练、微调与优化算法，从而改善偏差与代表性。\"},{\"question\":\"合成数据在社会文化层面为什么仍然存在局限？\",\"answer\":\"文中指出合成属性始终需要机器兼容性，因此缺失了其背后的社会文化动态与紧张关系。\"},{\"question\":\"论文从哪些维度分析“差异政治（politics of difference）”？\",\"answer\":\"论文通过三项核心维度展开批判性考察：解纠缠（disentanglement）、可组合性（compositionality）与规范性（normativity）。\"}]","Machine Learning, Synthetic Data, and the Politics of Difference | PDF",1785682983,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-synthetic-data-and-the-politics-of-difference","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-synthetic-data-and-the-politics-of-difference/118312/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"这篇论文研究的核心问题是什么？","Question",{"text":75,"@type":76},"论文探讨机器学习与AI中“相同与差异”的思想之间的关系，并说明合成数据与机器学习如何形成一种新的差异生成逻辑。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"论文如何概括合成数据与机器学习的“异质性/异向性（heterophilic）”逻辑？",{"text":80,"@type":76},"论文认为研究者倾向于主动生成差异与异质属性，用于训练、微调与优化算法，从而改善偏差与代表性。",{"name":82,"@type":73,"acceptedAnswer":83},"合成数据在社会文化层面为什么仍然存在局限？",{"text":84,"@type":76},"文中指出合成属性始终需要机器兼容性，因此缺失了其背后的社会文化动态与紧张关系。",{"name":86,"@type":73,"acceptedAnswer":87},"论文从哪些维度分析“差异政治（politics of difference）”？",{"text":88,"@type":76},"论文通过三项核心维度展开批判性考察：解纠缠（disentanglement）、可组合性（compositionality）与规范性（normativity）。","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]