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While machine learning is often thought of as a pipeline from data to decision, my research investigates the orthogonal pipeline from researcher decisions to impacts, both positive and negative, in broader society. The choices we make as researchers—how to collect data, which model architecture to use, how we evaluate performance or even how we define the question—result in different conclusions, different systems, and ultimately different outcomes for those our systems affect.
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论文共 11 篇作者统计合作学者相似作者
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2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS, ICCVWpp.4306-4314, (2023)
arxiv(2022)
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arxiv(2022)
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bioRxiv (Cold Spring Harbor Laboratory) (2020)
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INNOVATIVE USE OF NLP FOR BUILDING EDUCATIONAL APPLICATIONS (2019): 103-115
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