AnthonyMel Andrews on the connections between a naive belief in scientific objectivity (facts and data are "real" and "correct" and "neutral") and eugenics:
Francis Galton, pioneering figure of the eugenics movement, believed that good research practice should consist in “gathering as many facts as possible without any theory or general principle that might prejudice a neutral and objective view of these facts” (Jackson et al., 2005). Karl Pearson, statistician and fellow purveyor of eugenicist methods, approached research with a similar ethos: “theorizing about the material basis of heredity or the precise physiological or causal significance of observational results, Pearson argues, will do nothing but damage the progress of the science” (Pence, 2011). In collaborative work with Pearson, Weldon emphasised the superiority of data-driven methods which were capable of delivering truths about nature “without introducing any theory” (Weldon, 1895).
From The Immortal Science of ML: Machine Learning & the Theory-Free Ideal.
I've lost the reference, but I suspect it was Meredith Whittaker who's written and spoken about the big data turn at Google, where it was understood that having and collecting massive datasets allowed them to eschew model-building.
The core idea being critiqued here is that there's a kind of scientific view from nowhere: a theory-free, value-free, model-free, bias-free way of observing the world that will lead to Truth; and that it's the task of the scientist to approximate this view from nowhere as well as possible.
#AI #GenAI #GenerativeAI #LLMs #science #DataScience #ScientificObjectivity #eugenics #ViewFromNowhere
Francis Galton, pioneering figure of the eugenics movement, believed that good research practice should consist in “gathering as many facts as possible without any theory or general principle that might prejudice a neutral and objective view of these facts” (Jackson et al., 2005). Karl Pearson, statistician and fellow purveyor of eugenicist methods, approached research with a similar ethos: “theorizing about the material basis of heredity or the precise physiological or causal significance of observational results, Pearson argues, will do nothing but damage the progress of the science” (Pence, 2011). In collaborative work with Pearson, Weldon emphasised the superiority of data-driven methods which were capable of delivering truths about nature “without introducing any theory” (Weldon, 1895).
From The Immortal Science of ML: Machine Learning & the Theory-Free Ideal.
I've lost the reference, but I suspect it was Meredith Whittaker who's written and spoken about the big data turn at Google, where it was understood that having and collecting massive datasets allowed them to eschew model-building.
The core idea being critiqued here is that there's a kind of scientific view from nowhere: a theory-free, value-free, model-free, bias-free way of observing the world that will lead to Truth; and that it's the task of the scientist to approximate this view from nowhere as well as possible.
#AI #GenAI #GenerativeAI #LLMs #science #DataScience #ScientificObjectivity #eugenics #ViewFromNowhere
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