[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84577-en":3,"doc-seo-84577-105":29,"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":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":13,"seo_description":14,"update_tm":27,"read_time":28},84577,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems","Artificial intelligence is increasingly executing high-impact tasks through autonomous, opaque systems, creating ethical and legal risks that require trustworthy human–machine collaboration. The article proposes treating such systems as analogous to closely related animals: humans should not be framed as “users” or “deployers,” but as “handlers.” This role reframing clarifies traceable human responsibility and helps reduce “responsibility gaps.” It also addresses disanalogous elements and advocates for authentic human–AI teaming as collaborators pursuing complex goals.","arXiv :2607 .00523v 1 [ cs .HC] 1 Jul 2026  \nAI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems  \nNathan Gabriel Wood  \n[nathan.wood](nathan.wood@tuhh.de)[@](nathan.wood@tuhh.de)[tuhh.de](nathan.wood@tuhh.de)  \nAbstract  \nArtificial intelligence (AI) is becoming ubiquitous, and across domains, increasingly autonomous systems are carrying out tasks which raise significant ethical and legal challenges which demonstrate a need for strong human-machine teams rooted in trust. In this article, I argue that within highly impactful areas (such as medicine or warfighting) there are grounds for us initially treating autonomous and opaque systems as relevantly analogous to dogs (or other animals with which we have close relationships) . Under this analogy, humans making use of these systems are not to be viewed as “users” or “deployers” of these systems, but instead take the role of “handlers”. This recasting of roles shifts the way we view humans, AI-enabled and autonomous systems, and the relations between them, and moreover clarifies the clear and traceable lines of responsibility humans have for the outcomes brought about when using these systems. In developing this point, I clarify that the machine-animal analogy does admit disanalogous elements, but that its touch-points ground it as a starting point. I then explore how we can divest the humans-as-handlers approach of those aspects of our relationships with animals which are unfitting for how we engage with and make use of autonomous and AI-enabled systems. I conclude by arguing that the trajectory of human-machine teamings for autonomous and AI-enabled systems should be a state where we authentically view these not as artifacts which we simply make use of, but as collaborators with which we pursue complex goals and carryout complex tasks.  \nKeywords: Autonomous Systems, Artificial Intelligence, Trust, Opacity, Human-Machine Interaction, Autonomous Weapon Systems  \n1 Introduction  \nArtificial intelligence (AI) is becoming ubiquitous in modern society, from large language models (LLMs) streamlining everyday elements of business (Boiko et al., 2023; Fan et al., 2023; Hadi et al., 2023; Inagaki et al., 2023; Williams et al., 2023) to highly specialized AI systems being used in medical diagnostics (Johnson et al., 2021; Rajpurkar et al., 2022; King, 2023) . As AI enters into increasingly more domains, numerous ethical and legal challenges arise, ranging from copyright, data, and privacy concerns (notably, relating to the training of LLMs and image generation models (Stahl and Wright, 2018; Elliott and Soifer, 2022; Xiang, 2024)) to deep-seated worries about ethically weighty decisions being outsourced to machines (e.g., most notably within the medical and military domains; see respectively (Anom, 2020; Morley et al., 2020; Gundersen and Bærøe, 2022) and (Human Rights Watch, 2012; Sparrow, 2016; Roff and Danks, 2018; Human Rights Watch, 2018)) . Discussions of the impact of AI are further complicated by the fact that hype has caused many algorithmic processes to be touted as “AI”, even when these do not have any seeming “intelligence” to speak of (Soni et al. , 2020; Mattmann and Broderick, 2024) . Yet even granting that some of the AI boom is (over-)hyped, it is clear that artificial intelligence is reshaping the way we carry out many tasks which are ethically and legally significant, and this alone provides some cause for worry.  \nIn order to address these concerns, regulatory bodies, academic institutions, and professional associations have put forward a variety of AI ethics principles, best practices, or technical solutions.1 However, there still remain many pressing questions concerning how we may adequately team humans with autonomous and increasingly opaque AI-enabled systems. In societally impactful areas where lives will often be on the line (e.g., the medical and military domains), there is an especial need for robust methods of creati","cbCaioxKeu1wFd1h","https://ap.wps.com/l/cbCaioxKeu1wFd1h","pdf",454069,1,40,"English","en",105,"# Abstract\n# Introduction\n## AI Ethics and Legal Challenges\n## Need for Robust Human–Machine Teams\n## Humans-as-Handlers Analogy and Responsibility","[{\"question\":\"What problem does the paper focus on regarding autonomous and opaque AI systems?\",\"answer\":\"It focuses on ethical and legal challenges created when autonomous, opaque systems perform high-impact tasks, requiring strong human–machine teams grounded in trust.\"},{\"question\":\"How does the paper propose to reconceptualize the human role in using these systems?\",\"answer\":\"Humans should be treated as “handlers” rather than “users” or “deployers,” shifting expectations about what humans and machines are responsible for.\"},{\"question\":\"Why does the humans-as-handlers approach matter for accountability?\",\"answer\":\"It clarifies clear and traceable lines of responsibility for outcomes produced through using AI-enabled autonomous systems, helping mitigate concerns such as “responsibility gaps.”\"}]",1784196893,101,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"ai-trust-and-teaming-the-humans-as-handlers-approach-for-autonomous-and-opaque-ai-systems","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/ai-trust-and-teaming-the-humans-as-handlers-approach-for-autonomous-and-opaque-ai-systems/84577/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper focus on regarding autonomous and opaque AI systems?","Question",{"text":75,"@type":76},"It focuses on ethical and legal challenges created when autonomous, opaque systems perform high-impact tasks, requiring strong human–machine teams grounded in trust.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper propose to reconceptualize the human role in using these systems?",{"text":80,"@type":76},"Humans should be treated as “handlers” rather than “users” or “deployers,” shifting expectations about what humans and machines are responsible for.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the humans-as-handlers approach matter for accountability?",{"text":84,"@type":76},"It clarifies clear and traceable lines of responsibility for outcomes produced through using AI-enabled autonomous systems, helping mitigate concerns such as “responsibility 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