Are Scientists as Objective as Thermometers?
How objectivity works against understanding
Philosophers sometimes criticize simplistic interpretations of science, by pointing out that our conception of objective knowledge is flawed.
Put it this way: you’re being most subjective when you’re just venting your emotions, and you’re at your most objective when you’re working around your bias or ignoring your wishes and siding instead with logic and with the empirical evidence.
That’s fair enough.
But the more objective you’re being, or the more you’re letting the facts in the world speak for themselves, the less you’re understanding what you’re talking about.
Thus, a paradigm of objectivity would be a machine learning program, such as the kind that’s used in so-called artificial intelligence:
Machine learning starts with data — numbers, photos, or text, like bank transactions, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or [as] the information the machine learning model will be trained on…
From there, programmers choose a machine learning model to use, supply the data, and let the computer model train itself to find…
