[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290151-105":59,"doc-detail-290151-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","regulating-machine-learning-the-challenge-of-heterogeneity","Regulating Machine Learning - The Challenge of Heterogeneity","","Machine-learning algorithms increasingly support decisions across society, but they also create public concerns due to their autonomy and opacity. The difficulty of interpreting and explaining machine-learning outcomes can undermine trust in the fairness of AI-driven products and processes. Beyond opacity, heterogeneity is a central regulatory challenge: machine-learning systems differ widely by use, structure, and training data, and organizations often deploy multiple algorithm types together and update them over time.",{"@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/regulating-machine-learning-the-challenge-of-heterogeneity/290151/",{"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/regulating-machine-learning-the-challenge-of-heterogeneity/290151.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is regulating machine learning considered a public concern?","Question",{"text":112,"@type":113},"Because machine-learning systems operate autonomously and produce results that are difficult to interpret and explain, which can make it hard for the public to trust the fairness of algorithm-driven products or processes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What does “heterogeneity” mean in the context of this article?",{"text":117,"@type":113},"The article defines heterogeneity as the quality of consisting of dissimilar or diverse elements, and argues that differences among machine-learning systems determine when to regulate, who should regulate, and how regulations should be designed.",{"name":119,"@type":110,"acceptedAnswer":120},"What are major sources of heterogeneity in machine-learning systems?",{"text":121,"@type":113},"Heterogeneity arises from the highly diverse purposes machine-learning is used for, the many different forms of learning (such as supervised, unsupervised, semi-supervised, and reinforcement), and the way algorithms may be combined into suites that evolve and are updated over time.","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},290151,1789640803,{"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":44,"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,"preview_page_count":4},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","University of Pennsylvania Carey Law School  \nPenn Carey Law: Legal Scholarship Repository  \nFaculty Scholarship at Penn Carey Law  \n2-2023  \nRegulating Machine Learning: The Challenge of Heterogeneity  \nCary Coglianese  \nUniversity of Pennsylvania Carey Law School  \nFollow this and additional works at: [https://scholarship.law.upenn.edu/faculty_scholarship](https://scholarship.law.upenn.edu/faculty_scholarship)  \n Part of the Administrative Law Commons, Artificial Intelligence and Robotics Commons, Public Administration Commons, Science and Technology Studies Commons, and the Theory and Algorithms Commons  \nRepository Citation  \nCoglianese, Cary, \"Regulating Machine Learning: The Challenge of Heterogeneity\" (2023) . Faculty Scholarship at Penn Carey Law. 2921.  \n[https://scholarship.law.upenn.edu/faculty_scholarship/2921](https://scholarship.law.upenn.edu/faculty_scholarship/2921)  \nThis Article is brought to you for free and open access by Penn Carey Law: Legal Scholarship Repository. It has been accepted for inclusion in Faculty Scholarship at Penn Carey Law by an authorized administrator of Penn Carey Law: Legal Scholarship Repository. For more information, please [contact](contact PennlawIR@law.upenn.edu)[ PennlawIR@law.upenn.edu](contact PennlawIR@law.upenn.edu).  \nREGULATING MACHINE LEARNING:  \nTHE CHALLENGE OF HETEROGENEITY  \nBY  \nCARY COGLIANESE  \nEdward B. Shils Professor of Law and Professor of Political Science, and Director, Penn Program on Regulation, University of Pennsylvania.  \nMachine-learning algorithms increasingly drive technological advances that deliver valuable improvements for society and the economy. But these algorithms also raise important concerns. The way machine-learning algorithms work autonomously to find patterns in large datasets has given rise to fears of a world that will ultimately cede critical aspects of human control to the dictates of artificial intelligence. These fears seem only exacerbated by the intrinsic opacity surrounding how machine-learning algorithms achieve their results. To a greater degree than with other statistical tools, the outcomes generated by machine learning cannot be easily interpreted and explained, which can make it hard for the public to trust the fairness of products or processes powered by these algorithms.  \nFor these reasons, the autonomous and opaque qualities of machine-learning algorithms make these digital tools both distinctive and a matter of public concern. But when it comes to regulating machine learning, a different quality of these algorithms matters most of all: their heterogeneity. The Merriam-Webster Dictionary defines “heterogeneity” as“the quality or state of consisting of dissimilar or diverse elements.” Machine learning algorithms’ heterogeneity will make all the difference in deciding when to regulate them, who should regulate them, and how to design regulations imposed on their development and use.  \nMACHINE LEARNING’S HETEROGENEITY  \nOne of the most important sources of machine learning’s heterogeneity derives from the highly diverse uses to which it is put. These uses could hardly vary more widely. Consider just a small sample of ways that different entities use machine-learning algorithms:  \n· Social media platforms use them to select and highlight content for users;  \n· Hospital radiology departments use them to detect cancer in patients;  \n· Credit card companies use them to identify potential fraudulent charges;  \n· Commercial airlines use them to operate aircraft with auto-piloting systems;  \n· Online retailers use them to make product recommendations to visitors to their websites; and  \n· Political campaigns use them in deciding where and how advertise.  \nEven within the same organizations, different machinelearning algorithms can perform different functions. An automobile manufacturer, for example, might use one type of machine-learning algorithm to automate certain on-road operations of their vehicles, while using other machinel","cbCainlOAMbiie60","https://ap.wps.com/l/cbCainlOAMbiie60","pdf",522814,13,"English","# Machine Learning’s Heterogeneity\n## Diverse uses of machine-learning algorithms\n## Different algorithm types and model forms\n## Same model, different applications\n## Combined algorithms and evolving systems","[{\"question\":\"Why is regulating machine learning considered a public concern?\",\"answer\":\"Because machine-learning systems operate autonomously and produce results that are difficult to interpret and explain, which can make it hard for the public to trust the fairness of algorithm-driven products or processes.\"},{\"question\":\"What does “heterogeneity” mean in the context of this article?\",\"answer\":\"The article defines heterogeneity as the quality of consisting of dissimilar or diverse elements, and argues that differences among machine-learning systems determine when to regulate, who should regulate, and how regulations should be designed.\"},{\"question\":\"What are major sources of heterogeneity in machine-learning systems?\",\"answer\":\"Heterogeneity arises from the highly diverse purposes machine-learning is used for, the many different forms of learning (such as supervised, unsupervised, semi-supervised, and reinforcement), and the way algorithms may be combined into suites that evolve and are updated over time.\"}]","Regulating Machine Learning - The Challenge of Heterogeneity | PDF",33]