[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117208-en":3,"doc-seo-117208-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},117208,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine-Learning Fairness in Data Markets - Challenges and Opportunities - Doctor of Philosophy Thesis","Machine learning promises to unlock substantial economic value, yet widespread deployment intensifies fairness concerns that must be addressed for social benefits to be distributed equitably. This thesis studies machine-learning fairness in the economic setting of data markets, analyzing how fairness interventions should be designed to account for market incentives, intervention effects, and their interaction. Results show that ignoring data-market economics can increase efficiency losses beyond what fairness requires, even risking market collapse. Under favorable conditions or with appropriate information, these losses can be recovered or amortized, enabling fairness at lower relative efficiency cost than previously understood.","Machine-Learning Fairness in Data Markets: Challenges and Opportunities  \nRoland Maio  \nSubmitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nunder the Executive Committee  \nof the Graduate School of Arts and Sciences  \nCOLUMBIA UNIVERSITY  \n© 2024 Roland Maio  \nAll Rights Reserved  \nAbstract  \nMachine-Learning Fairness in Data Markets: Challenges and Opportunities  \nRoland Maio  \nMachine learning promises to unlock troves of economic value. As advanced  \nmachine-learning techniques proliferate, they raise acute fairness concerns. These concerns must be addressed in order for the economic surpluses and externalities generated by machine learning to benefit society equitably. In this thesis, we focus on the economic context of data markets and theoretically study the impacts of intervening to achieve machine-learning fairness. We find that to effectively and efficiently intervene requires taking the data market into account in the design and application of the fairness intervention, i.e., how the intervention impacts the data market, how the data market impacts the intervention, and how their impacts interact. We study this interaction in two data-market settings to understand what information is necessary. We find that without taking into account the incentive structure and economics of a data market, fairness interventions can induce greater losses to efficiency than are necessary to achieve fairness—even potentially inducing market collapse. Yet, we also find that these losses can be recovered or even amortized away by suitably designing the intervention with appropriate information or under favorable market conditions. Overall, this thesis elucidates how data markets present both novel challenges and opportunities for machine-learning fairness. It demonstrates that efficiently intervening for machine-learning fairness can be more complicated in data markets—even infeasible! Excitingly, however, it also demonstrates that under favorable market conditions, fairness can be achieved at lower relative cost to efficiency than has previously been understood to  \nbe possible. We hope that these initial theoretical findings ultimately contribute to the  \ndevelopment of efficient and practical fairness interventions suitable for real-world application.  \nTable of Contents  \nAcknowledgments ........................................ v  \n[Dedication](Dedication ............................................ vi)[ ............................................](Dedication ............................................ vi)[ vi](Dedication ............................................ vi)  \n[Chapter 1: Introduction](Chapter 1: Introduction ...................................)[ ...................................](Chapter 1: Introduction ...................................). 1  \n1.1 Can pure machine-learning solutions be applied out-of-the-box in data markets? .. 7  \n1.2 How do market conditions impact fairness interventions? .............. 10  \nChapter 2: Incentives Needed for Low-Cost Fair Lateral Data Reuse ............ 12  \n2.1 Introduction ...................................... 12  \n2.2 Related Work ..................................... 16  \n2.3 Utility of Fair Representations ............................ 17  \n2.4 The Cost of Demographic Secrecy .......................... 21  \n2.4.1 One Data Consuming Firm .......................... 23  \n2.4.2 Multiple Data Consuming Firms ....................... 25  \n2.5 Gains of Incentivizing Fairness ............................ 31  \n2.6 Discussion ....................................... 40  \n2.6.1 Do accuracy gains generalize? ........................ 40  \n2.6.2 What are impediment and limitations? .................... 42  \n2.6.3 Data Reuse and Composition ......................... 43  \n2.7 Conclusion ...................................... 44  \nChapter 3: The Cost of Fair Production in a Data Market ................... 45  \n3.1 Introduction .........................","cbCailE673yaNa9c","https://ap.wps.com/l/cbCailE673yaNa9c","pdf",534345,1,139,"English","en",105,"# Acknowledgments\n# Dedication\n# Chapter 1: Introduction\n## Can pure machine-learning solutions be applied out-of-the-box in data markets?\n## How do market conditions impact fairness interventions?\n# Chapter 2: Incentives Needed for Low-Cost Fair Lateral Data Reuse\n## Introduction\n## Related Work\n## Utility of Fair Representations\n## The Cost of Demographic Secrecy\n## Gains of Incentivizing Fairness\n## Discussion\n## Conclusion\n# Chapter 3: The Cost of Fair Production in a Data Market\n## Introduction\n## Related Literature\n## Model\n## Data market equilibria under N buyers\n## Intervening for fairness can backfire\n## Market growth can mitigate backfire risk and amortize the cost of fairness\n# References\n# Appendix A: Probabilistic Inequalities","[{\"question\":\"Why does machine learning require fairness interventions in data markets?\",\"answer\":\"Machine-learning deployment generates economic value while simultaneously creating fairness concerns. To benefit society equitably, interventions must address fairness without harming efficiency unnecessarily in the data-market setting.\"},{\"question\":\"What goes wrong if fairness interventions ignore data-market incentives?\",\"answer\":\"Ignoring the incentive structure and economics of a data market can cause efficiency losses greater than needed to achieve fairness. In extreme cases, such interventions may even induce market collapse.\"},{\"question\":\"How can fairness be achieved with lower efficiency cost?\",\"answer\":\"By designing interventions that account for how the intervention affects the data market and how the market affects the intervention, losses can be recovered or amortized. Under favorable market conditions, fairness can be achieved at a lower relative cost to efficiency.\"}]","Machine-Learning Fairness in Data Markets - Challenges and Opportunities - Doctor of Philosophy Thesis | PDF",1785674417,350,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-fairness-in-data-markets-challenges-and-opportunities-doctor-of-philosophy-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-fairness-in-data-markets-challenges-and-opportunities-doctor-of-philosophy-thesis/117208/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does machine learning require fairness interventions in data markets?","Question",{"text":76,"@type":77},"Machine-learning deployment generates economic value while simultaneously creating fairness concerns. To benefit society equitably, interventions must address fairness without harming efficiency unnecessarily in the data-market setting.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What goes wrong if fairness interventions ignore data-market incentives?",{"text":81,"@type":77},"Ignoring the incentive structure and economics of a data market can cause efficiency losses greater than needed to achieve fairness. In extreme cases, such interventions may even induce market collapse.",{"name":83,"@type":74,"acceptedAnswer":84},"How can fairness be achieved with lower efficiency cost?",{"text":85,"@type":77},"By designing interventions that account for how the intervention affects the data market and how the market affects the intervention, losses can be recovered or amortized. 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