6 Quotes About Feedback Loop

Will those insights be tested, or simply used to justify the status quo and reinforce prejudices? When I consider the sloppy and self-serving ways that companies use data, I'm often reminded of phrenology, a pseudoscience that was briefly the rage in the nineteenth century. Phrenologists would run their fingers over the patient's skull, probing for bumps and indentations. Each one, they thought, was linked to personality traits that existed in twenty-seven regions of the brain. Usually the conclusion of the phrenologist jibed with the observations he made. If the patient was morbidly anxious or suffering from alcoholism, the skull probe would usually find bumps and dips that correlated with that observation - which, in turn, bolstered faith in the science of phrenology. Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. Models like the ones that red-lighted Kyle Behm and black-balled foreign medical students and St. George's can lock people out, even when the "science" inside them is little more than a bundle of untested assumptions. Cathy ONeil

Will those insights be tested, or simply used to justify the status quo and reinforce prejudices? When I consider the sloppy and self-serving ways that companies use data, I'm often reminded of phrenology, a pseudoscience that was briefly the rage in the nineteenth century. Phrenologists would run their fingers over the patient's skull, probing for bumps and indentations. Each one, they thought, was linked to personality traits that existed in twenty-seven regions of the brain. Usually the conclusion of the phrenologist jibed with the observations he made. If the patient was morbidly anxious or suffering from alcoholism, the skull probe would usually find bumps and dips that correlated with that observation - which, in turn, bolstered faith in the science of phrenology. Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. Models like the ones that red-lighted Kyle Behm and black-balled foreign medical students and St. George's can lock people out, even when the "science" inside them is little more than a bundle of untested assumptions. Cathy ONeil

From a mathematical point of view, however, trust is hard to quantify. That's a challenge for people building models. Sadly, it's far easier to keep counting arrests, to build models that assume we're birds of a feather and treat us as such. Innocent people surrounded by criminals get treated badly, and criminals surrounded by law-abiding public get a pass. And because of the strong correlation between poverty and reported crime, the poor continue to get caught up in the digital dragnets. The rest of us barely have to think about them. Cathy ONeil

Because we bump into reinforcing loops so often, it is handy to know this shortcut: The time it takes for an exponentially growing stock to double in size, the “doubling time, ” equals approximately 70 divided by the growth rate (expressed as a percentage). Example: If you put $100 in the bank at 7% interest per year, you will double your money in 10 years (70 ÷ 7 = 10). If you get only 5% interest, your money will take 14 years to double. Donella H. Meadows