[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126795-en":3,"doc-seo-126795-105":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":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},126795,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Current Topological and Machine Learning Applications for Bias Detection in Text","Explores how stigma and multiple forms of bias shape societal and clinical outcomes, distinguishing implicit and explicit biases and describing how they can become formal through laws, educational assumptions, and institutional barriers. Discusses solutions to implicit bias, including antibias training and diversity-focused interventions that show lasting effects. Connects bias detection with natural language processing: tokenization, term matching, embeddings such as TF-IDF, word2vec, GloVe, and transformer-based models like BERT for turning text into machine-learning-ready representations.","Current Topological and Machine Learning Applications for Bias Detection in Text  \nColleen Farrelly∗ , Yashbir Singh†, Quincy A. Hathaway‡, Gunnar Carlsson§ , Ashok Choudhary¶ , Rahul Paul ∥ , Gianfranco Doretto‡, Yassine Himeur∗∗ , Shadi Atalls∗∗ , and Wathiq Mansoor∗∗  \n∗ Staticlysm LLC, Miami, FL, USA  \n† Radiology, Mayo Clinic, Rochester, MN  \n‡ West Virginia University, Morgantown, WV, USA  \n§ Stanford University, California, USA  \n¶Department of surgery, Mayo Clinic, Rochester, MN, USA  \nIEEEauthorblockA ∥Harvard University, USA  \nIEEEauthorblockA∗∗ College of Engineering and Information Technology, University of Dubai, Dubai, UAE  \nA. Stigma and Bias  \nStigma is a perceived identity within a society or subgroup arising from a mismatch between social identities valued by those in power within a society and a person’s true identity [9] . Examples include physical characteristics (such as race or missing limb differences), medical disorders (such as substance use disorders or visual impairment), and changeable visual cues of subgroups (such as tattoos or mohawks within a society where the majority and those in power do not display these visual cues) . Stigma can be associated with two forms  \nof bias: 1) implicit biases, where the person within a majority group acts with bias but is unaware that they are acting on their bias against the stigmatized group [10], and 2) explicit biases, where the person within the majority group acts with bias and is aware that they are acting on their bias against the stigmatized group [11] . Thus, stigmatized groups often face unique societal challengesbecause of implicit and explicit biases, whichcan happen formally or informally.  \nB. Institutional Bias  \nCharacteristics devalued by those in power or by the majority in society as different than the society’s norms become codified into laws and regulations within societal institutions, and individuals with those characteristics often face institutional barriers [9] . For instance, a student with visual impairment in an educational system that assumes all students can see will run into problems with many academic tasks, like reading assignments or the blackboard, unless another option is created; fortunately, this is usually the result of an implicit bias that is quickly identified by the educational system [12] . Jim Crow laws in the American South exemplify explicit bias within the legal system [13] .  \nC. Non-Institutional Bias  \nDevaluation of individuals based on characteristics can also be more informal with respect to everyday interactions. For instance, a physician treating a patient with a history of substance use disorder which presents with pain may quickly label the patient as “drug-seeking,” which then follows the patient throughout their journey to receive a diagnosis [14],[15]; hopefully, this is an implicit bias and not an intentional dismissal of a patient. Implicit bias against female patients has been well-documented as leading to adverse outcomes for patients presenting in emergency settings with symptoms that diverge from androcentric cardiac symptoms in emergency settings [16]–[18] . Both types of informal biases often become codified in the clinical record by physicians, creating institutional barriers through documented language by those in authority [19]–[22]  \nD. Implicit Bias Solutions  \nBecause implicit bias is not intentional, antibias training work effectively in combatting bias and have lasting effects [23], [24] . Within the medical setting, implicit biases against patients can be ameliorated through diversity training, such as in Morris et al., 2019, which provided a curriculum for working with LGBTQIA+ patients for current students. Interventions like this can likely assuage the issues in other fields, such as the legal field or education. Creating inclusive environments has also been shown to combat biases [25] .  \nE. Natural Language Processing  \nOne branch of machine learning, natural language processing","cbCais0yVc9NVJCo","https://ap.wps.com/l/cbCais0yVc9NVJCo","pdf",514142,1,6,"English","en",105,"# Stigma and Bias\n# Institutional Bias\n# Non-Institutional Bias\n# Implicit Bias Solutions\n# Natural Language Processing","[{\"question\":\"What is the difference between implicit and explicit bias in this text?\",\"answer\":\"Implicit bias involves biased actions without awareness of holding the bias against a stigmatized group. Explicit bias involves biased actions with awareness of applying the bias.\"},{\"question\":\"How does institutional bias arise according to the document?\",\"answer\":\"Institutional bias forms when societal norms devalue certain characteristics and this devaluation becomes embedded in laws, regulations, and barriers that individuals face in institutions like education or healthcare.\"},{\"question\":\"What NLP steps are described for bias detection in text?\",\"answer\":\"Text is processed via tokenization, tokens are matched to terms of interest using pre-trained recognition or custom dictionaries, and documents are transformed into embeddings (e.g., TF-IDF, word2vec, GloVe, or BERT) for supervised learning models.\"}]","Current Topological and Machine Learning Applications for Bias Detection in Text | PDF",1785934827,15,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"current-topological-and-machine-learning-applications-for-bias-detection-in-text","",{"@graph":36,"@context":85},[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/current-topological-and-machine-learning-applications-for-bias-detection-in-text/126795/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the difference between implicit and explicit bias in this text?","Question",{"text":75,"@type":76},"Implicit bias involves biased actions without awareness of holding the bias against a stigmatized group. Explicit bias involves biased actions with awareness of applying the bias.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does institutional bias arise according to the document?",{"text":80,"@type":76},"Institutional bias forms when societal norms devalue certain characteristics and this devaluation becomes embedded in laws, regulations, and barriers that individuals face in institutions like education or healthcare.",{"name":82,"@type":73,"acceptedAnswer":83},"What NLP steps are described for bias detection in text?",{"text":84,"@type":76},"Text is processed via tokenization, tokens are matched to terms of interest using pre-trained recognition or custom dictionaries, and documents are transformed into embeddings (e.g., TF-IDF, word2vec, GloVe, or BERT) for supervised learning models.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]