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CAMEO: Explaining Consensus and Expertise Across MOdels

dc.contributor.authorYu, Andy
dc.date.accessioned2024-05-02T14:25:28Z
dc.date.available2024-05-02T14:25:28Z
dc.date.issued2024-05-02
dc.date.submitted2024-04-26
dc.description.abstractExplainable AI methods have been proposed to help interpret complex models, e.g., by assigning importance scores to model features or perturbing the features in a way that changes the prediction. These methods apply to one model at a time, but in practice, engineers usually select from many candidate models and hyperparameters. To assist with this task, we formulate a space of comparison operations for multiple models and demonstrate CAMEO: a web-based tool that explains consensus and expertise among multiple models. Users can interact with CAMEO using a variety of models and datasets, to explore 1) consensus patterns, such as subsets of the test dataset or intervals within feature domains where models disagree, 2) data perturbations that would make conflicting models agree (and consistent models disagree), and 3) expertise patterns, such as subsets of the test dataset where a particular model has surprising performance compared with other models.en
dc.identifier.urihttp://hdl.handle.net/10012/20534
dc.language.isoenen
dc.pendingfalse
dc.publisherUniversity of Waterlooen
dc.subjectexplainabilityen
dc.subjectcomparisonsen
dc.subjectmodel performanceen
dc.subjectmodel biasen
dc.titleCAMEO: Explaining Consensus and Expertise Across MOdelsen
dc.typeMaster Thesisen
uws-etd.degreeMaster of Mathematicsen
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.embargo.terms0en
uws.contributor.advisorGolab, Lukasz
uws.contributor.affiliation1Faculty of Mathematicsen
uws.peerReviewStatusUnrevieweden
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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