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Filtering by: Creator Osmundson, Todd W. Remove constraint Creator: Osmundson, Todd W. Advisor Burnett, Margaret M. Remove constraint Advisor: Burnett, Margaret M. Degree Field Computer Science Remove constraint Degree Field: Computer Science

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  • Personalizing machine learning systems with explanatory debugging

    Creator:
    Kulesza, Todd
    Abstract:
    How can end users efficiently influence the predictions that machine learning systems make on their behalf? Traditional systems rely on users to provide examples of how they want the learning system to behave, but this is not always practical for the user, nor efficient for the learning system. This dissertation...
    Resource Type:
    Dissertation
    Full Text:
    Debugging AN ABSTRACT OF THE DISSERTATION OF Todd Kulesza for the degree of Doctor of Philosophy in

  • Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

    Creator:
    Dodge, Jonathan E.
    Abstract:
    Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
    Resource Type:
    Dissertation
    Full Text:
    nearest half-hour, and records are presented as [W, L D]. Tables G.1–G.4 all will use a similar format

  • How does AAR/AI Support Problem Solvers with Diverse Behaviors and Cognitive Styles?

    Creator:
    Dikkala, Rupika
    Abstract:
    "What’s wrong with this AI?" Explainable AI (XAI) researchers are moving beyond explaining an AI’s actions, to helping users detect an AI’s failures. However this detection may not be enough—for actionability, we often need to pinpoint which part failed. We investigate how AAR/AI, a structured assessment process, supports users with...
    Resource Type:
    Masters Thesis

  • Understanding and supporting end-user debugging strategies

    Creator:
    Grigoreanu, Valentina I.
    Abstract:
    End users' programs are fraught with errors, costing companies millions of dollars. One reason may be that researchers and tool designers have not yet focused on end-user debugging strategies. To investigate this possibility, this dissertation presents eight empirical studies and a new strategy-based end-user debugging tool for Excel, called StratCel....
    Resource Type:
    Dissertation
    Full Text:
    data collection and analysis for that chapter. Jill Cao, Todd Kulesza, and Chris Bogart helped