[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117202-en":3,"doc-seo-117202-105":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},117202,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Fairness in Streaming Submodular Maximization over a Matroid Constraint","Streaming submodular maximization provides a natural framework for selecting representative subsets from large datasets under streaming constraints. When items carry sensitive attributes like gender or race, enforcing fairness becomes essential to limit bias and discrimination. Building on prior work for monotone submodular maximization with cardinality, this paper studies the generalized setting under a matroid constraint, presenting streaming algorithms plus impossibility results that expose trade-offs among efficiency, solution quality, and fairness, validated on real-world applications.","Fairness in Streaming Submodular Maximization over a Matroid Constraint  \nMarwa El Halabi * 1 Federico Fusco * 2 Ashkan Norouzi-Fard * 3 Jakab Tardos * 3 4 Jakub Tarnawski * 5  \nAbstract  \nStreaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as gender or race, it becomes important to enforce fairness to avoid bias and discrimination. This has spurred significant interest in developing fair machine learning algorithms. Recently, such algorithms have been developed for monotone submodular maximization under a cardinality constraint. In this paper, we study the natural generalization of this problem to a matroid constraint. We give streaming algorithms as well as impossibility results that provide trade-offs between efficiency, quality and fairness. We validate our findings empirically on a range of well-known real-world applications:  \nexemplar-based clustering, movie recommendation, and maximum coverage in social networks.  \n1. Introduction  \nRecent years have seen a growing trend of utilizing machine learning algorithms to support or replace human decisionmaking. An undesirable effect of this phenomenon is the potential for bias and discrimination in automated decisions, especially in sensitive domains such as hiring, access to credit and education, bail decisions, and law enforcement (Munoz et al., 2016; White House OSTP, 2022; European Union FRA, 2022) . In order to attenuate such risks, the computer science community has been working on developing fair algorithms for fundamental tasks such as classification (Zafar et al., 2017), ranking (Celis et al., 2018c; Singh & Joachims, 2019), clustering (Chierichetti et al., 2017;  \n*Equal contribution 1 Samsung - SAIT AI Lab, Montreal 2 Sapienza University of Rome 3 Google Zurich 4EPFL 5Microsoft Research. Correspondence to: Marwa El Halabi \u003C[marwa.elhalabi@gmail.com](marwa.elhalabi@gmail.com) >, Federico Fusco \u003Cfus[cof@diag.uniroma1.it](cof@diag.uniroma1.it) >, Ashkan Norouzi-Fard \u003Cashkan[norouzi@google.com](norouzi@google.com) >, Jakab Tardos \u003C[tardos@google.com](tardos@google.com) >, Jakub Tarnawski \u003C[jatarnaw@microsoft.com](jatarnaw@microsoft.com) >.  \nProceedings of the 40 th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023 . Copyright 2023 by the author(s) .  \nBackurs et al., 2019; Bhm et al., 2021; Jia et al., 2022; Anegg et al., 2022; Angelidakis et al., 2022), online learning (Joseph et al., 2016; Chzhen et al., 2021), voting (Celiset al., 2018a), matching (Chierichetti et al., 2019), influence maximization (Tsang et al., 2019; Rahmattalabi et al., 2021), data summarization (Celis et al., 2018b), online selection (Correa et al., 2021), and graph problems (Rahmattalabiet al., 2019; Anagnostopoulos et al., 2020) .  \nIn this paper, we study fairness in the fundamental problem of streaming monotone submodular maximization over a matroid constraint. Submodularity is a well-studied property of set functions that captures the natural notion of diminishing returns and has found vast applications in machine learning, including active learning (Golovin & Krause, 2011), data summarization (Lin & Bilmes, 2011), feature selection (Das & Kempe, 2011), and recommender systems (El-Arini & Guestrin, 2011) . Matroids are a popular and powerful class of independence systems, capturing a wide range of useful constraints such as cardinality, block cardinality, linear independence, and connectivity constraints. In all of the above applications, it is crucial to have the capacity to handle the massive volume of modern datasets, which are often produced so rapidly that they cannot even be stored in memory. This has motivated a long line of work on the streaming setting.  \nWithout considering fairness, maximizing a monotone submodular function over a matroid constraint is a well-studied problem. While a tight (1 − 1/e)-approximation is known i","cbCaiv2szSGM4fZU","https://ap.wps.com/l/cbCaiv2szSGM4fZU","pdf",525367,1,22,"English","en",105,"# Abstract\n# Introduction\n## Our contribution","[{\"question\":\"What problem does the paper study?\",\"answer\":\"The paper studies fairness in streaming monotone submodular maximization under a matroid constraint, aiming to control bias when selecting representative subsets from large datasets.\"},{\"question\":\"How is fairness defined in this work?\",\"answer\":\"Fairness is defined via color groups of items: a solution set must meet lower and upper bounds on how many selected items fall into each sensitive-attribute group.\"},{\"question\":\"What do the paper’s main results provide?\",\"answer\":\"The paper provides streaming algorithms and impossibility results, establishing trade-offs among memory/computation efficiency, solution quality, and fairness guarantees, and validates them on applications like clustering, recommendation, and social-network coverage.\"}]","Fairness in Streaming Submodular Maximization over a Matroid Constraint | 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problem does the paper study?","Question",{"text":75,"@type":76},"The paper studies fairness in streaming monotone submodular maximization under a matroid constraint, aiming to control bias when selecting representative subsets from large datasets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is fairness defined in this work?",{"text":80,"@type":76},"Fairness is defined via color groups of items: a solution set must meet lower and upper bounds on how many selected items fall into each sensitive-attribute group.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the paper’s main results provide?",{"text":84,"@type":76},"The paper provides streaming algorithms and impossibility results, establishing trade-offs among memory/computation efficiency, solution quality, and fairness guarantees, and validates them on applications like clustering, recommendation, and social-network 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