[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-136442-105":59,"doc-detail-136442-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","explainability-does-not-mitigate-the-negative-impact-of-incorrect-ai-advice-in-a-personnel-selection-task","Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task","","Decision support systems using artificial intelligence are increasingly used in personnel selection, yet their effects on decision-making remain insufficiently understood. Five experiments with students and HRM employees tested how people interact with correct versus incorrect AI advice, including manipulations of advice source (human vs. AI) and explainability format (heatmaps and charts). Incorrect advice consistently harmed task performance through overreliance, while source and explainability effects were limited, highlighting the need for regulation and quality standards in HRM.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/explainability-does-not-mitigate-the-negative-impact-of-incorrect-ai-advice-in-a-personnel-selection-task/136442/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/explainability-does-not-mitigate-the-negative-impact-of-incorrect-ai-advice-in-a-personnel-selection-task/136442.png","ImageObject",300,407,{"name":92,"@type":93},"Xiajie","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-22",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was investigated in the experiments on AI advice for personnel selection?","Question",{"text":112,"@type":113},"The study examined how people use correct versus incorrect AI-generated advice in a personnel selection task, including how they respond to different advice sources and explainability formats.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did incorrect advice affect participants’ performance?",{"text":117,"@type":113},"Incorrect advice negatively impacted task performance because participants failed to dismiss it, indicating overreliance on inaccurate guidance.",{"name":119,"@type":110,"acceptedAnswer":120},"Did explainability reduce the negative impact of incorrect AI advice?",{"text":121,"@type":113},"No. The lack of a reduction in overreliance when predictions were made more explainable indicates that explainability alone does not mitigate the harm of incorrect advice in this context.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},136442,1787386858,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},8814010472675,"https://avatar.qwps.com/avatar/WGlhamll","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nExplainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task  \nJulia Cecil1*, Eva Lermer1,2, Matthias F. C. Hudecek 3, Jan Sauer4 & Susanne Gaube1,5  \nDespite the rise of decision support systems enabled by artificial intelligence (AI) in personnel selection, their impact on decision-making processes is largely unknown. Consequently, we conducted five experiments (N = 1403 students and Human Resource Management (HRM) employees)  \ninvestigating how people interact with AI-generated advice in a personnel selection task. In all pre-registered experiments, we presented correct and incorrect advice. In Experiments 1a and 1b, we manipulated the source of the advice (human vs. AI) . In Experiments 2a, 2b, and 2c, we further manipulated the type of explainability of AI advice (2a and 2b: heatmaps and 2c: charts) .  \nWe hypothesized that accurate and explainable advice improves decision-making. The independent variables were regressed on task performance, perceived advice quality and confidence ratings. The results consistently showed that incorrect advice negatively impacted performance, as people failed to dismiss it (i.e., overreliance). Additionally, we found that the effects of source and explainability of advice on the dependent variables were limited. The lack of reduction in participants’ overreliance on inaccurate advice when the systems’ predictions were made more explainable highlights the complexity of human-AI interaction and the need for regulation and quality standards in HRM.  \nKeywords Artificial intelligence, Personnel selection, Decision-making  \nThe use of decision support systems enabled by artificial intelligence (AI) has proliferated in various areas, ranging from healthcare to human resource management (HRM1) . One area in HRM in which AI systems are already used is resume screening in personnel selection2, which represents an ideal application of AI due to the sheer amount of available data3,4. However, research on the usage of AI systems in HRM is currently limited, and findings have been mixed5,6. Given that personnel decisions substantially impact organizational performance7 and individuals’ lives, it is crucial to understand how AI-generated advice influences high-stakes decisions and to optimize the interaction between AI systems and their users. This need for a better understanding of the interaction between AI-enabled systems and humans is particularly high when AI algorithms produce advice that humans should consider when making important decisions8. Consequently, the willingness to rely on advice plays a pivotal role in integrating AI-generated predictions into human decision-making processes.  \nIt is well-established that people often rely on and are heavily affected by advice9–11. When advice is presented, many psychological phenomena come into play: for instance, the anchoring effect12, where advice can serve asa starting point (i.e., anchor) from which people make decisions and sometimes fail to adjust sufficiently. Even after an initial decision has been made, advice can lead people to alter their decision toward the given value8, 10. AI-enabled decision support systems are often used in critical contexts where incorrect adjustments from the advice can have far-reaching consequences, e.g., in medical care or personnel selection. Therefore, it is crucial to understand how different advice characteristics contribute to its adoption and how erroneous reliance can be prevented.  \nFirst, the source of advice can affect people’s reliance on it. On the one hand, studies across different domains demonstrated a preference for human over AI advice8, 13, i.e., people reject algorithm advice more often than human advice, even when the human is obviously inferior to the algorithm (algorithmic aversion14) . On the other hand, it has also been shown that individuals are more willing to adh","cbCainUTxIOqmNXr","https://ap.wps.com/l/cbCainUTxIOqmNXr","pdf",2955151,15,"English","# Study overview\n## Experimental design and manipulations\n## Hypotheses and measures\n## Findings on incorrect advice and overreliance\n## Role of advice source and explainability\n## Background: advice use and automation bias","[{\"question\":\"What was investigated in the experiments on AI advice for personnel selection?\",\"answer\":\"The study examined how people use correct versus incorrect AI-generated advice in a personnel selection task, including how they respond to different advice sources and explainability formats.\"},{\"question\":\"How did incorrect advice affect participants’ performance?\",\"answer\":\"Incorrect advice negatively impacted task performance because participants failed to dismiss it, indicating overreliance on inaccurate guidance.\"},{\"question\":\"Did explainability reduce the negative impact of incorrect AI advice?\",\"answer\":\"No. The lack of a reduction in overreliance when predictions were made more explainable indicates that explainability alone does not mitigate the harm of incorrect advice in this context.\"}]","Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task | PDF",38]