I once sat in a room with a bunch of machine learning folks who were developing creative artificial intelligence to make “good art.” I asked one researcher about the training data. How did they choose to operationalize “good art”? Their reply: they used Netflix data about engagement hours. (View Highlight)
Consider, for example, a policy proposal that doctors should urge patients to sharply lower their saturated fat intake. This should lead to better health outcomes, at least for those that are easier to measure: heart attack numbers and average longevity. But the focus on easy-to-measure outcomes often diminishes the salience of other downstream consequences: the loss of culinary traditions, disconnection from a culinary heritage, and a reduction in daily culinary joy. It’s easy to dismiss such things as “intangibles.” But actually, what’s more tangible than a good cheese, or a cheerful fondue party with friends? (View Highlight)
La buena vida.
Data collection techniques must be repeatable across vast scales. They require standardized categories. Repeatability and standardization make data-based methods powerful, but that power has a price. It limits the kinds of information we can collect. (View Highlight)
Data is supposed to be consistent and stable across contexts. The methodology of data requires leaving out some of our more sensitive and dynamic ways of understanding the world in order to achieve that stability. (View Highlight)