1,138 research outputs found

    Recalibrating machine learning for social biases: demonstrating a new methodology through a case study classifying gender biases in archival documentation

    Get PDF
    This thesis proposes a recalibration of Machine Learning for social biases to minimize harms from existing approaches and practices in the field. Prioritizing quality over quantity, accuracy over efficiency, representativeness over convenience, and situated thinking over universal thinking, the thesis demonstrates an alternative approach to creating Machine Learning models. Drawing on GLAM, the Humanities, the Social Sciences, and Design, the thesis focuses on understanding and communicating biases in a specific use case. 11,888 metadata descriptions from the University of Edinburgh Heritage Collections' Archives catalog were manually annotated for gender biases and text classification models were then trained on the resulting dataset of 55,260 annotations. Evaluations of the models' performance demonstrates that annotating gender biases can be automated; however, the subjectivity of bias as a concept complicates the generalizability of any one approach. The contributions are: (1) an interdisciplinary and participatory Bias-Aware Methodology, (2) a Taxonomy of Gendered and Gender Biased Language, (3) data annotated for gender biased language, (4) gender biased text classification models, and (5) a human-centered approach to model evaluation. The contributions have implications for Machine Learning, demonstrating how bias is inherent to all data and models; more specifically for Natural Language Processing, providing an annotation taxonomy, annotated datasets and classification models for analyzing gender biased language at scale; for the Gallery, Library, Archives, and Museum sector, offering guidance to institutions seeking to reconcile with histories of marginalizing communities through their documentation practices; and for historians, who utilize cultural heritage documentation to study and interpret the past. Through a real-world application of the Bias-Aware Methodology in a case study, the thesis illustrates the need to shift away from removing social biases and towards acknowledging them, creating data and models that surface the uncertainty and multiplicity characteristic of human societies

    From abuse to trust and back again

    Get PDF
    oai:westminsterresearch.westminster.ac.uk:w7qv

    2023 GREAT Day Program

    Get PDF
    SUNY Geneseo’s Seventeenth Annual GREAT Day. Geneseo Recognizing Excellence, Achievement & Talent Day is a college-wide symposium celebrating the creative and scholarly endeavors of our students. http://www.geneseo.edu/great_dayhttps://knightscholar.geneseo.edu/program-2007/1017/thumbnail.jp

    Southern Adventist University Undergraduate Catalog 2022-2023

    Get PDF
    Southern Adventist University\u27s undergraduate catalog for the academic year 2022-2023.https://knowledge.e.southern.edu/undergrad_catalog/1121/thumbnail.jp

    Para-texts: Alterity and Infected Reading in Jeff VanderMeer’s Southern Reach Trilogy

    Get PDF
    This thesis examines Jeff VanderMeer's Southern Reach Trilogy within an ecocritical and deconstructive framework. Published in quick succession in 2014, the trilogy – composed of Annihilation, Authority and Acceptance – traces the shadowy outline of ‘Area X’, the name given to a mysterious stretch of coast along the Eastern Seaboard. While the official explanation for Area X is an ecological disaster, the reality is much weirder; inside Area X, things transform. Drawing predominantly on Annihilation, I explore how acts of writing manifest within Area X, as well as how this language ‘infects’ the narrative tissue of the trilogy itself. By situating writing outside the human body, I argue that The Southern Reach Trilogy represents writing – and thus language more broadly – as a distinctly nonhuman alterity

    Science and Innovations for Food Systems Transformation

    Get PDF
    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems
    • …
    corecore