UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Boyd, Karen L

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    Understanding and Intervening in Machine Learning Ethics: Supporting Ethical Sensitivity in Training Data Curation
    (2020) Boyd, Karen L; Shilton, Katie; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Despite a great deal of attention to developing mitigations for ethical concerns in Machine Learning (ML) training data and models, we don’t yet know how these interventions will be adopted and used. Will they help ML engineers find and address ethical concerns in their work? This dissertation seeks to understand ML engineers’ ethical sensitivity (ES)— their propensity to notice, analyze, and act on socially impactful aspects of their work—while curating training data. A systematic review of ES (Chapter 2) addresses conflicts of conceptualization in prior work by developing a new framework describing three activities (recognition, particularization, and judgment); argues that ES offers a useful way to describe, evaluate, and intervene in ethical technology development; and argues that the methods and perspectives of social computing can offer richer methods and data to studies of ES. A think aloud study (Chapter 3) tests this framework by using ES to compare engineers working with unfamiliar training data, finding that engineers with Datasheets noticed ethical issues earlier and more frequently than those without; finding that participants relied on Datasheets extensively while particularizing; and rendering rich descriptions of recognition and particularization in facial recognition data curation. Chapter 4 uses Value Sensitive Design to "design up,'' mitigating harms by helping machine learning engineers particularize their ethical concerns and find appropriate technical tools. It introduces ES to studies of social computing, contributes a novel method for studying ES, offers rich data about how it functions in ML development, describes insights for designing context documents and other interventions designed to encourage ES, develops an extensible digital guide that supports particularization and judgment, and points to new directions for research in ethical sensitivity in technology development.