[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117952-en":3,"doc-seo-117952-105":30,"detail-sidebar-cat-0-en-105":83},{"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},117952,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Using machine learning to decode animal communication - New methods promise transformative insights and conservation benefits","The text discusses how AI systems in clinical care can reflect and amplify latent biases, shaping patient outcomes and clinician decisions. It outlines strategies such as individualized performance feedback for clinicians, transparent patient notification about AI tools, and research to determine what and how much information should be disclosed. It also covers regulatory considerations for mandatory bias evaluation, the importance of examining AI’s own fairness tools, and the need for implementation research to understand contextual conditions that allow bias to emerge.","SPECIAL SECTION A MACHINE-INTELLIGENT WORLD  \nthe performance of the model itself (11). Increasing clinician awareness of AI’s biases is critical, but this desire maybe paradoxical: Knowing about biases in AI may result in less willingness to use AI-based recommendations for patients that a clinician judges“different” from others. Assuming models are biased in terms of race or ethnicity, for example, could result in clinicians systematically overriding a model’s recommendation for that group of patients.  \nSeveral strategies exist to identify and address latent biases. One strategy could involve providing clinicians with modelspecific, individual-level performance feedback regarding whether they tend to outperform or underperform it, or if they are systematically following or overriding a model only for certain patient groups. Individualized feedback has the potential to improve clinician performance (12) . However, a challenge for assessing bias is that clinicians may not see sufficient numbers of patients in different groups to allow rigorous, stratified comparisons.  \nPatients should be informed about the use of AI in their clinical care as a matter of respect. This includes general messaging about the use of predictive algorithms, chatbots, and other AI-based technologies, and specific notification when new AIbased technologies are used in their individual care. Doing so may improve awareness of AI, motivate conversations with clinicians, and support greater transparency around AI use.  \nExactly how much to disclose, and in what format, are unanswered questions that require additional research. There is a need to avoid AI exceptionalism—the idea that AI is riskier or requires greater protection, just because it is AI—and presently patients want to know more, not less (8). That other decisions relying on algorithms, such as clinical risk calculators or computer-aided radiographic or electrocardiogram interpretation, may not be routinely shared with patients is not an argument in favor of secrecy.  \nBias has not been a major aspect of drug and device regulations, which focus on overall safety and efficacy. Recent US proposals could extend legal liability to physicians and hospitals, meaning they could be required to provide compensatory damages to patients or be subject to penalties for use of biased clinical algorithms; these could be applied to AI algorithms (13) . However, the complexity of AI algorithmsand persistent ethical disagreement over when differential performance by race or ethnicity equals true bias complicate liability proposals. Drug and device regulatory agencies might consider making evaluations of bias mandatory for approval (14) .  \nA first step could be requiring evaluations of differential performance and bias under different real-world assumptions in approval processes and other forums, such as in journal reporting of AI research.  \nIn addition, the gaze of AI should be turned on itself. This requires proactive, intentional development of AI tools to identify biases in AI and in its clinical implementation (15). AI may contribute to the emergence of biases, but it also has the potential to detect biases and hence facilitate new ways of overcoming them. Open-source tools, such as AI Fairness 360, FairML, and others, show promise in helping researchers assess fairness in their machine learning data and algorithms. These tools can assess biases in datasets, predictive outputs, and even the different techniques that can be used to mitigate bias according to different metrics of fairness. Their application to health care data and algorithms deserves rigorous scientific examination.  \nImplementation research is urgently needed to better understand the role of different contextual factors and latent conditions in allowing biases to emerge. Exactly which patients may experience bias under which circumstances requires ongoing rigorous study. In AI, biased data and biased algorithms result in biased outcomes for","cbCaipIIranMX688","https://ap.wps.com/l/cbCaipIIranMX688","pdf",1429796,1,5,"English","en",105,"# SPECIAL SECTION A MACHINE-INTELLIGENT WORLD\n## Strategies to identify and address latent biases\n## Patient communication and disclosure of AI use\n## Regulation, liability, and mandatory bias evaluation\n## Open-source tools and proactive AI fairness development\n## Implementation research for contextual fairness","[{\"question\":\"What is meant by avoiding “AI exceptionalism,” and how does it relate to patient disclosure?\",\"answer\":\"AI exceptionalism treats AI as riskier and requiring greater protection simply because it is AI. The text states that patients want more information rather than less, and that withholding sharing of algorithm-based decisions is not a justification for secrecy.\"}]","Using machine learning to decode animal communication - New methods promise transformative insights and conservation benefits | PDF",1785680511,13,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"using-machine-learning-to-decode-animal-communication-new-methods-promise-transformative-insights-and-conservation-benefits","",{"@graph":36,"@context":77},[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/using-machine-learning-to-decode-animal-communication-new-methods-promise-transformative-insights-and-conservation-benefits/117952/",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],{"name":72,"@type":73,"acceptedAnswer":74},"What is meant by avoiding “AI exceptionalism,” and how does it relate to patient disclosure?","Question",{"text":75,"@type":76},"AI exceptionalism treats AI as riskier and requiring greater protection simply because it is AI. 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