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Linus Huang
University of California, San Diego
  1.  13
    Engineering Equity: How AI Can Help Reduce the Harm of Implicit Bias.Ying-Tung Lin, Tzu-Wei Hung & Linus Ta-Lun Huang - forthcoming - Philosophy and Technology:1-26.
    This paper focuses on the potential of “equitech”—AI technology that improves equity. Recently, interventions have been developed to reduce the harm of implicit bias, the automatic form of stereotype or prejudice that contributes to injustice. However, these interventions—some of which are assisted by AI-related technology—have significant limitations, including unintended negative consequences and general inefficacy. To overcome these limitations, we propose a two-dimensional framework to assess current AI-assisted interventions and explore promising new ones. We begin by using the case of human (...)
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  2.  50
    The Insularity of Anglophone Philosophy: Quantitative Analyses.Eric Schwitzgebel, Linus Ta-Lun Huang, Andrew Higgins & Ivan Gonzalez-Cabrera - 2018 - Philosophical Papers 47 (1):21-48.
    We present evidence that mainstream Anglophone philosophy is insular in the sense that participants in this academic tradition tend mostly to cite or interact with other participants in this academic tradition, while having little academic interaction with philosophers writing in other languages. Among our evidence: In a sample of articles from elite Anglophone philosophy journals, 97% of citations are citations of work originally written in English; 96% of members of editorial boards of elite Anglophone philosophy journals are housed in majority-Anglophone (...)
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    Can Massive Modularity Explain Human Intelligence? Information Control Problem and Implications for Cognitive Architecture.Linus Ta-Lun Huang - forthcoming - Synthese:1-30.
    A fundamental task for any prospective cognitive architecture is information control: routing information to the relevant mechanisms to support a variety of tasks. Jerry Fodor has argued that the Massive Modularity Hypothesis cannot account for flexible information control due to its architectural commitments and its reliance on heuristic information processing. I argue instead that the real trouble lies in its commitment to nativism—recent massive modularity models, despite incorporating mechanisms for learning and self-organization, still cannot learn to control information flexibly enough. (...)
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