[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83578-en":3,"doc-seo-83578-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83578,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Structuring the Space of Sociotechnical Alignment: A Specification Framework and Systematic Literature Review","Sociotechnical alignment addresses how AI should satisfy socially desirable behavior, making it inherently normative rather than purely technical. NLP research often treats “social desirability” as insufficiently defined, leaving unclear how alignment defines, justifies, and evaluates desired behavior. The work proposes a human-centered specification framework grounded in sociobehavioral desirability theories. A systematic literature review of 281 papers identifies recurring issues: missing or conflated normative concepts, underdefined target populations, and rarely justified design choices, limiting cumulative progress.","Structuring the Space of Sociotechnical Alignment: A Specification Framework and Systematic Literature Review  \nEsra Dönmez and Agnieszka Falenska  \nInstitute for Natural Language Processing, University of Stuttgart Interchange Forum for Reflecting on Intelligent Systems, University of Stuttgart  \n[esra.doenmez@ims.uni-stuttgart.de](esra.doenmez@ims.uni-stuttgart.de)  \narXiv :2607 .0 1250v 1 [ cs .CY] 2 Jun 2026  \nAbstract  \nSociotechnical alignment concerns the social desirability of AI behavior and is thus inherently normative, not merely technical.  \nWhile NLP research increasingly addresses its technical aspects, it often leaves underspecified what such “social desirability” entails. We argue that this reflects a fundamental gap: the absence of a systematic way to specify how sociotechnical alignment defines, justifies, and evaluates socially desirable AI behavior.  \nTo address this gap, we introduce a humancentered framework for specifying sociotechnical alignment. We draw on socialscientific accounts of sociobehavioral desirability to ground the basis for behavioral desirability judgments and use this framework to analyze how alignment is specified in practice. Our systematic literature review identifies recurring patterns: normative concepts grounding desirability judgments are often unspecified or conflated with alignment targets for (desired) system behavior, target populations are underdefined, and design choices are rarely theoretically justified. These findings point to a lack of conceptual specificity that limits cumulative progress. We therefore offer recommendations that link social-scientific frameworks to alignment design choices, supporting more conceptually precise approaches to sociotechnical alignment.  \n1 Introduction  \nAs AI systems become embedded in social contexts, they must meet not only functional goals, such as producing accurate recommendations, but also normative expectations about how they should “behave” in society, for example, by being fair and respectful in their interactions (Selbstet al., 2019 ; Weidinger et al., 2024) . This broader challenge of specifying, modeling, and evaluating  \nFigure 1: Framework for specifying sociotechnical alignment: overarching theoretical framework at the top, with three dimensions below: alignment target, normative concept, and alignment methodology, each with examples at the bottom.  \nsocially desirable AI behavior across social contexts is referred to as sociotechnical alignment.  \nDespite growing work on sociotechnical alignment, the literature still leaves “social desirability”underspecified. First, “alignment” is often used as a catch-all term for heterogeneous, socially contested objectives, ranging from user engagement to social acceptability (Gabriel and Ghazavi, 2023) . Second, existing work often moves between different ways of specifying what AI systems should “align to”, ranging from normative concepts (e.g., human values) to more instrumental proxies (e.g., user preferences or stakeholder requirements), without specifying the reasoning behind these choices (Ji et al., 2025b) . This ambiguity is worsened when alignment is framed primarily in terms of “human values”, blurring technical questions of system behavior with normative disputes over whose values matter and how they are interpreted (Gabriel, 2020 ; Sorensenet al., 2024) . Work on pluralistic alignment further shows that evaluation criteria (how alignment is assessed) and relevant populations (whose judgments are considered) cannot be treated as universal (Rastogi et al., 2025) . Taken together, these issues point to a deeper conceptual gap: the absence of a systematic way to specify how sociotech-  \nnical alignment defines, justifies, and evaluates socially desirable AI behavior.  \nThe present work addresses this gap by structuring sociotechnical alignment along four interrelated dimensions (Figure 1): the alignment target specifying desired system behavior, the normative concept defining ","cbCaivHjJQx2cHGq","https://ap.wps.com/l/cbCaivHjJQx2cHGq","pdf",1042593,2,1,42,"English","en",105,"# Abstract\n# Introduction\n# Specification Framework\n# Systematic Literature Review","[{\"question\":\"What gap does the paper identify in current sociotechnical alignment research?\",\"answer\":\"It identifies the lack of a systematic way to specify how sociotechnical alignment defines, justifies, and evaluates socially desirable AI behavior.\"},{\"question\":\"How does the proposed framework structure sociotechnical alignment?\",\"answer\":\"It organizes alignment along four interrelated dimensions: alignment target, normative concept, alignment methodology, and a theoretical framework justifying choices across contexts and populations.\"},{\"question\":\"What recurring patterns does the systematic literature review find?\",\"answer\":\"The review finds that normative concepts are often unspecified or conflated with alignment targets, target populations are frequently underdefined, and design choices are rarely theoretically 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