[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117464-en":3,"doc-seo-117464-105":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},117464,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Peopleware - Designing Machine Learning for Human Realities","Peopleware: Designing Machine Learning for Human Realities investigates how machine learning systems should account for human limitations, intent, and biases in practical interaction settings. The dissertation develops methods for generating aspect-aware comparative sentences from user reviews to support efficient decision-making, and proposes AI-assisted decision-making that minimizes user decision effort while reducing fatigue. It also examines model and human bias issues, constructs datasets for evaluation, and presents experimental results, limitations, and conclusions to guide human-centered and human-compatible learning design.","UC San Diego  \nUC San Diego Electronic Theses and Dissertations  \nTitle  \nPeopleware: Designing Machine Learning for Human Realities  \nPermalink  \n[https://escholarship.org/uc/item/2cc1b18z](https://escholarship.org/uc/item/2cc1b18z)  \nAuthor  \nEchterhoff, Jessica Maria  \nPublication Date  \n2025  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA SAN DIEGO  \nPeopleware: Designing Machine Learning for Human Realities  \nA dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy  \nin  \nComputer Science  \nby  \nJessica Maria Echterhoff  \nCommittee in charge:  \nProfessor Julian McAuley, Chair  \nProfessor Michael Coblenz  \nProfessor Gary Cottrell  \nProfessor Berk Ustun  \nCopyright  \nJessica Maria Echterhoff, 2025 All rights reserved.  \nThe Dissertation of Jessica Maria Echterhoff is approved, and it is acceptable in quality and form for publication on microfilm and electronically.  \nUniversity of California San Diego  \n2025  \nDEDICATION  \nTo everyone who thinks they can’t do it – but then does it anyway.  \nTABLE OF CONTENTS  \nDissertation Approval Page .................................................... iii  \nDedication .................................................................. iv  \nTable of Contents ............................................................ v  \nList of Figures ............................................................... ix  \nList of Tables ................................................................ xi  \nAcknowledgements ........................................................... xiii  \nVita ........................................................................ xv  \nAbstract of the Dissertation .................................................... xvi  \nChapter 1 Introduction ..................................................... 1  \n1.1 Summary ........................................................... 1  \n1.1.1 Human Inefficiencies ........................................... 2  \n1.1.2 Human Intent ................................................. 3  \n1.1.3 Model Bias ................................................... 3  \n1.1.4 Human Bias .................................................. 4  \nChapter 2 Background ..................................................... 5  \n2.1 Human-Centered Machine Learning ..................................... 5  \n2.2 Human-Compatible Machine Learning ................................... 6  \n2.3 Human Error, Flaws and Biases ......................................... 7  \n2.4 Behavior Sequence Modeling .......................................... 8  \n2.5 Large Language Models ............................................... 9  \nChapter 3 Generating Aspect-Aware Comparative Sentences from User Reviews for Efficient Comparison ............................................. 11  \n3.1 Introduction ......................................................... 11  \n3.2 Related Work ........................................................ 13  \n3.2.1 Natural Language Recommendation Generation .................... 13  \n3.2.2 Comparisons as a Relative Evaluation Method ...................... 14  \n3.2.3 Natural Language Comparisons .................................. 15  \n3.3 Dataset Construction .................................................. 16  \n3.3.1 Comparative Sentence Extraction ................................. 16  \n3.3.2 Automatically Labeling Comparative Sentences ..................... 17  \n3.3.3 Obtaining Item Aspects ......................................... 18  \n3.4 Comparative Sentence Generation ....................................... 18  \n3.4.1 Personalized Aspect-Guided Generation (AGG) .................... 18  \n3.4.2 Baseline ...................................................... 20  \n3.4.3 Evaluation .................................................... 21  \n3.5 Results ..................................","cbCaipaKn3gaOdGs","https://ap.wps.com/l/cbCaipaKn3gaOdGs","pdf",21250611,1,165,"English","en",105,"# Chapter 1 Introduction\n## Summary\n## Human Inefficiencies\n## Human Intent\n## Model Bias\n## Human Bias\n# Chapter 2 Background\n## Human-Centered Machine Learning\n## Human-Compatible Machine Learning\n## Human Error, Flaws and Biases\n## Behavior Sequence Modeling\n## Large Language Models\n# Chapter 3 Generating Aspect-Aware Comparative Sentences from User Reviews for Efficient Comparison\n## Introduction\n## Related Work\n## Dataset Construction\n## Comparative Sentence Generation\n## Personalized Aspect-Guided Generation (AGG)\n## Baseline\n## Evaluation\n## Results\n## Limitations\n## Conclusion\n# Chapter 4 Minimizing Decision Effort with AI Assisted Decision-Making\n## Introduction\n## Related Work\n## Decision Minimizer Network\n## Sequential Decision-Making with Markov Decision Processes\n## Experiments\n## Results","[{\"question\":\"What problem does the dissertation address about machine learning and humans?\",\"answer\":\"It focuses on designing machine learning that reflects human realities, including human inefficiencies, intent, and biases that can affect system behavior and outcomes.\"},{\"question\":\"How does the dissertation generate comparative statements for user decisions?\",\"answer\":\"It generates aspect-aware comparative sentences from user reviews, using dataset construction, comparative sentence extraction and labeling, and personalized aspect-guided generation (AGG) for efficient comparison.\"},{\"question\":\"What is the goal of the AI-assisted decision-making approach?\",\"answer\":\"The approach aims to minimize user decision effort by modeling decision fatigue states and decision trajectories, then evaluating results against baselines and decision-step reduction strategies.\"}]","Peopleware - Designing Machine Learning for Human Realities | 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