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The Human Genome Diversity Project : what went wrong?

In Sandra G. Harding (ed.), The postcolonial science and technology studies reader. Durham: Duke University Press (2011)

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  1. Data Science as Machinic Neoplatonism.Dan McQuillan - 2018 - Philosophy and Technology 31 (2):253-272.
    Data science is not simply a method but an organising idea. Commitment to the new paradigm overrides concerns caused by collateral damage, and only a counterculture can constitute an effective critique. Understanding data science requires an appreciation of what algorithms actually do; in particular, how machine learning learns. The resulting ‘insight through opacity’ drives the observable problems of algorithmic discrimination and the evasion of due process. But attempts to stem the tide have not grasped the nature of data science as (...)
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  • How does the consideration of Indigenous identities in the US complicate conversations about tracking folk racial categories in epidemiologic research?Shelbi Nahwilet Meissner - 2018 - Synthese 198 (Suppl 10):2439-2462.
    In public health research, tracking folk racial categories (in disease risk, etc.) is a double-edged tool. On the one hand, tracking folk racial categories is dangerous because it reinforces a problematic but fairly common belief in biological race essentialism. On the other hand, ignoring racial categories also runs the risk of ignoring very real biological phenomena in which marginalized communities, likely in virtue of their marginalization, are sicker and in need of improved resources. Much of the conversation among epidemiologists and (...)
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