[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118613-en":3,"doc-seo-118613-105":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},118613,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",6,"Technology","Machine Learning Applications and Sustainable Development - Machine Learning for Societal Improvement, Modernization and Progress","Presentation on machine learning applied to societal improvement, modernization, and progress, framed through sustainability and responsible AI considerations. Uses real-world privacy concerns as motivation, illustrating how large-scale data analysis can enable profiling, re-identification, and inference about individuals from seemingly removed or aggregated records. Explains the generalization vs. personalization tension in model behavior and introduces privacy concepts such as k-anonymity and related anonymity notions, including challenges in dataset generalization and computational complexity.","MAC H IN E  \nLEA RN IN G  \nAP P L I CAT I ON S  \nAN D S USTAINAB L E  \nDEV E L OPM ENT  \nVishnu S . Pendyala, Ph . D .  \nSan Jose State University  \nTo cite this presentation: Pendyala, V.S.(2023)“Machine Learning Applications and Sustainable Development” University of Bolton, UK, November to Remember Webinar series.  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License  \nThis Photo by Unknown Author is licensed under CC BY  \nLast Year’s November2remember  \nMachine Learning for Societal Improvement, Modernization and Progress  \n[https://www.youtube.com/watch?v=RdoguqaM8PA](https://www.youtube.com/watch?v=RdoguqaM8PA)  \nMore Background  \nExploring Equity, Diversity, Inclusion, and Social Justice in AI: A Comprehensive Lens  \n[https://www.youtube.com/watch?v=YKKOEc3nwAs](https://www.youtube.com/watch?v=YKKOEc3nwAs)  \nThis Photo by Unknown Author is licensed under CC BY  \n3  \n“It contained facial recognition records and ID scans for about 2.5 million people, mostly in Urumqi, a city with a population of about 3.5 million.”  \n“The system sifted through billions of records, then displayed details of her education, family ties, links to an earlier case and recent  \nvisits to a hotel and an internet cafe.”  \n“There have been countless instances of recordings featuring private discussions between doctors and patients, business deals, seemingly criminal dealings, sexual encounters and so on. These recordings are accompanied by user  \ndata showing location, contact details, and app data.”  \nSource: [https://www.theguardian.com/technology/2019/jul/26/apple-contractors-regularly-hear-confidential-details-on-siri-recordings](https://www.theguardian.com/technology/2019/jul/26/apple-contractors-regularly-hear-confidential-details-on-siri-recordings)  \nMachine Learning: From Generalization to Personalization  \nAnalyze many  \nApply to one  \nperson X is 90% of the time polite, 83% magnanimous,…  \nAre they good or  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License bad?  \nTraining data  \nTest data  \nBut what if    \nAnalyze many datasets Identify one  \nor more  \nperson X has numb fingers, is looking for single men, has some weird information needs…  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License  \nThere are queries for “landscapers in Lilburn, Ga,” several people with the last name Arnold and “homes sold in shadow lake subdivision gwinnett county georgia.  \nIt did not take much investigating to follow that data trail to Thelma Arnold, a 62-year-old widow who lives in Lilburn, Ga.”  \n…  \n“No. 3505202 asks about “depression and medical leave.” No. 7268042  \ntypes “fear that spouse contemplating cheating.”  \nBarth-Jones, D. (2012) . The're-identification'of governor William Weld's medical information: a critical re-examination of health data identification risks and privacy protections, then and now. Then and Now (July 2012) .  \n\"At the time GIC released the data, William Weld, then Governor of Massachusetts, assured the public that GIC had protected patient privacy by  deleting identifiers. In response, then graduate student Sweeney started hunting for the Governor’s hospital records in the GIC data. She knew that Governor Weld resided in Cambridge, Massachusetts, a city of 54,000 residents and seven ZIP codes. For twenty dollars, she purchased the complete voter rolls from the city of Cambridge, a database containing, among other things, the name, address, ZIP code, birth date, and sex of every voter. By combining this data with  the GIC records, Sweeney found Governor Weld with ease. Only six people in Cambridge shared his birth date, only three of them men, and of them, only he lived in his ZIP code. In a theatrical flourish, Dr. Sweeney sent the Governor’s  health records (which included diagnoses and prescriptions) to his office. \"  \n􀀡-An","cbCaida17SVMi0TK","https://ap.wps.com/l/cbCaida17SVMi0TK","pdf",8794300,1,25,"English","en",105,"# Machine Learning Applications and Sustainable Development\n## Machine Learning for societal improvement, modernization and progress\n## Exploring equity, diversity, inclusion, and social justice in AI\n## Machine learning: generalization to personalization\n## Privacy risks in real-world data\n## Anonymity and k-anonymity definitions\n## K-anonymity example and challenges","[{\"question\":\"How does the presentation connect machine learning to societal improvement and progress?\",\"answer\":\"It frames machine learning use cases around societal modernization and progress, then links AI deployments to broader goals such as equity and social justice. The discussion emphasizes how model behavior and data practices affect real communities.\"},{\"question\":\"Why are privacy risks central to the talk?\",\"answer\":\"It highlights that analyzing large datasets can reveal sensitive personal details and enable re-identification, even when systems claim identifiers are removed. Real examples illustrate how background knowledge and cross-dataset linkage can uncover identities.\"},{\"question\":\"What is k-anonymity, and what challenges does the presentation mention?\",\"answer\":\"It defines k-anonymity using the idea of indistinguishability via quasi-identifiers. It also notes issues such as loss of information from generalization, vulnerability to re-identification with background knowledge, and computational difficulty in checking or generalizing datasets.\"}]","Machine Learning Applications and Sustainable Development - Machine Learning for Societal Improvement, Modernization and Progress | PDF",1785684510,63,{"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},"machine-learning-applications-and-sustainable-development-machine-learning-for-societal-improvement-modernization-and-progress","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-applications-and-sustainable-development-machine-learning-for-societal-improvement-modernization-and-progress/118613/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the presentation connect machine learning to societal improvement and progress?","Question",{"text":75,"@type":76},"It frames machine learning use cases around societal modernization and progress, then links AI deployments to broader goals such as equity and social justice. The discussion emphasizes how model behavior and data practices affect real communities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are privacy risks central to the talk?",{"text":80,"@type":76},"It highlights that analyzing large datasets can reveal sensitive personal details and enable re-identification, even when systems claim identifiers are removed. Real examples illustrate how background knowledge and cross-dataset linkage can uncover identities.",{"name":82,"@type":73,"acceptedAnswer":83},"What is k-anonymity, and what challenges does the presentation mention?",{"text":84,"@type":76},"It defines k-anonymity using the idea of indistinguishability via quasi-identifiers. It also notes issues such as loss of information from generalization, vulnerability to re-identification with background knowledge, and computational difficulty in checking or generalizing datasets.","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]