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What does it take to build medical machine learning models that work equally well for all patients? Or, inversely, why do models often work better for some patient groups than others? This is what we set out to answer in our recent Cell Press Patterns perspective.
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TL;DR: Calibration by group and error rate parity across groups are compatible.
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I recently had the opportunity to write a book review essay for Prometheus – Critical Studies in Innovation. The book I reviewed is “Is AI good for the planet?” by Benedetta Brevini (2021), to be found here. I’d like to thank Steven Umbrello, the Prometheus book review editor, for entrusting me this review!
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… really, yet another post about about maximum likelihood (ML) estimation? Well – yes; I could not find a source that summarized exactly the things I needed to know, so here it is. What will you find?
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Recently, I wanted to create boxplots from a data file for a paper I was writing using pgfplots. Turns out that’s more difficult than expected, especially since the (otherwise very useful) documentation is a bit meager in this point.