(ML)²: Myths and Legends of Machine Learning


Skepticism is (and should be) a vital part of any science; statistics and data science are no exception. Statistician George Box nicely summed it up when he said, “all models are wrong, but some are useful”. Box reminds us that statistical models are just that: models. A simplified representation of the real-world will always have shortcomings. But we shouldn’t forget the last bit of Box’s saying: “some [models] are useful”. Although challenging, carefully constructed statistical models can be extremely…

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Consider Your Biases

In the wake of Donald Trump’s victory over Hillary Clinton, pundits and politicians alike have wondered, “how did we not predict this?” Theories range from misrepresentative polling to journalistic bias to confirmation bias, fueled by the echo chambers of social media. These fervent debates about bias in politics had me reflecting on the role that bias plays in science and in R&D. Sampling bias, expectancy bias, publication bias… all hazards of the profession and yet science is held up against other disciplines as relatively bias-free by virtue of its data-centric approach.

Biopharma R&D has rapidly evolved over the last few years — it is more collaborative, demands greater speed to respond to competition, and challenges many notions of “conventional” drug discovery. In my reflections, I was curious whether this rapid evolution was a harbinger of biases not conventionally associated with science — and wanted to understand how we at twoXAR aim to stay aware and ahead of such biases.

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