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Enhancing Usability and Explainability of Data Systems

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dc.contributor.advisorAlexandra Meliou
dc.contributor.authorFariha, Anna
dc.contributor.departmentUniversity of Massachusetts Amherst
dc.date2024-03-27T17:41:28.000
dc.date.accessioned2024-04-26T15:47:52Z
dc.date.available2024-04-26T15:47:52Z
dc.date.submittedSeptember
dc.date.submitted2021
dc.description.abstractThe recent growth of data science expanded its reach to an ever-growing user base of nonexperts, increasing the need for usability, understandability, and explainability in these systems. Enhancing usability makes data systems accessible to people with different skills and backgrounds alike, leading to democratization of data systems. Furthermore, proper understanding of data and data-driven systems is necessary for the users to trust the function of the systems that learn from data. Finally, data systems should be transparent: when a data system behaves unexpectedly or malfunctions, the users deserve proper explanation of what caused the observed incident. Unfortunately, most existing data systems offer limited usability and support for explanations: these systems are usable only by experts with sound technical skills, and even expert users are hindered by the lack of transparency into the systems' inner workings and functions. The aim of my thesis is to bridge the usability gap between nonexpert users and complex data systems, aid all sort of users, including the expert ones, in data and system understanding, and provide explanations that help reason about unexpected outcomes involving data systems. Specifically, my thesis has the following three goals: (1) enhancing usability of data systems for nonexperts, (2) enable data understanding that can assist users in a variety of tasks such as achieving trust in data-driven machine learning, gaining data understanding, and data cleaning, and (3) explaining causes of unexpected outcomes involving data and data systems. For enhancing usability, we focus on example-driven user intent discovery. We develop systems based on example-driven interactions in two different settings: querying relational databases and personalized document summarization. Towards data understanding, we develop a new data-profiling primitive that can characterize tuples for which a machine-learned model is likely to produce untrustworthy predictions. We also develop an explanation framework to explain causes of such untrustworthy predictions. Additionally, this new data-profiling primitive enables interactive data cleaning. Finally, we develop two explanation frameworks, tailored to provide explanations in debugging data system components, including the data itself. The explanation frameworks focus on explaining the root cause of a concurrent application's intermittent failure and exposing issues in the data that cause a data-driven system to malfunction.
dc.description.degreeDoctor of Philosophy (PhD)
dc.description.departmentComputer Science
dc.identifier.doihttps://doi.org/10.7275/24064407
dc.identifier.orcidhttps://orcid.org/0000-0002-5275-7844
dc.identifier.urihttps://hdl.handle.net/20.500.14394/18627
dc.relation.urlhttps://scholarworks.umass.edu/cgi/viewcontent.cgi?article=3357&context=dissertations_2&unstamped=1
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.source.statuspublished
dc.subjectdatabase
dc.subjectusability
dc.subjectexplainability
dc.subjecttrust
dc.subjectdebugging
dc.subjectDatabases and Information Systems
dc.subjectSoftware Engineering
dc.titleEnhancing Usability and Explainability of Data Systems
dc.typeopenaccess
dc.typearticle
dc.typedissertation
digcom.contributor.authorisAuthorOfPublication|email:anna.fariha.bd@gmail.com|institution:University of Massachusetts Amherst|Fariha, Anna
digcom.identifierdissertations_2/2311
digcom.identifier.contextkey24064407
digcom.identifier.submissionpathdissertations_2/2311
dspace.entity.typePublication
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