[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122390-en":3,"doc-seo-122390-105":30,"detail-sidebar-cat-0-en-105":90},{"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},122390,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","On the Edge-Balancing Privacy and Fairness in Machine Learning","This document presents a visual metaphor for the challenges in Machine Learning (ML) deployment, specifically focusing on the tension between privacy and fairness. The image depicts a performer on a unicycle amidst flames, symbolizing the ML model's precarious balance on unstable real-world data. The flames represent the conflicting demands of user privacy and algorithmic fairness, highlighting how addressing one can compromise the other. The performer's act of balancing fire symbolizes the core research objective: finding an optimal equilibrium between privacy, fairness, and model utility. The dark surroundings emphasize the inherent uncertainties, adversarial threats, and unforeseen consequences in high-stakes ML applications, particularly in critical domains like healthcare and criminal justice. The image was created for the Images of Research Competition at the University of Alberta in 2025, captured on a Google Pixel 8, and post-processed with Snapseed. The image was taken at the Fringe Festival in Edmonton on August 20, 2024.","On the Edge: Balancing Privacy and Fairness in Machine Learning  \nSemi-Finalist  \nRabeya Bosri Computing Science Master's program  \nImages of Research Competition University of Alberta 2025  \nImage Description  \nThis image illustrates the delicate balancing act that Machine Learning systems encounter in real-world deployment. The performer represents an ML model, while the unicycle beneath them symbolizes the unstable foundation of real-world data, which is often messy and full of implicit biases. The flames on either side represent the conflicting needs for privacy and fairness in ML; both are important but can often clash with each other. Ensuring a model is fair for everyone can sometimes put user privacy at risk. Balancing on a unicycle while holding fi re represents the main challenge of my research: fi nding the proper middle ground between privacy, fairness, and how useful the model is. The surrounding darkness suggests the constant uncertainty of adversarial threats and the unpredictable consequences of design decisions in high-stakes ML applications such as healthcare and criminal justice.  \nImage Location  \nThe image was taken at the Fringe Festival Edmonton, August 20, 2024.  \nImage Creation  \nI captured this photo on my model phone (Google Pixel 8) and made some post-processing (doing some colour correction) in a mobile app named Snapseed.  \nImages of Research Competition University of Alberta 2025","cbCaivAwVzAN8trA","https://ap.wps.com/l/cbCaivAwVzAN8trA","pdf",396557,1,2,"English","en",105,"# On the Edge: Balancing Privacy and Fairness in Machine Learning\n## Image Description\n## Image Location\n## Image Creation","[{\"question\":\"What does the performer on the unicycle represent in the image?\",\"answer\":\"The performer on the unicycle represents a Machine Learning (ML) model, with the unicycle symbolizing the unstable foundation of real-world data that is often messy and biased.\"},{\"question\":\"What do the flames on either side of the performer symbolize?\",\"answer\":\"The flames symbolize the conflicting needs for privacy and fairness in Machine Learning, illustrating how ensuring fairness can sometimes put user privacy at risk.\"},{\"question\":\"What is the main challenge of the research as represented by the image?\",\"answer\":\"The main challenge, represented by balancing on a unicycle while holding fire, is finding the proper middle ground between privacy, fairness, and the overall usefulness of the ML model.\"}]","On the Edge-Balancing Privacy and Fairness in Machine Learning | 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