“However, when you create a model from proxies, it is far simpler for people to game it. This is because proxies are easier to manipulate than the complicated reality they represent.”
― Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
― Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
“If they want to flirt or initiate a friendship, they should carefully avoid giving the impression they are taking the initiative; men do not like tomboys, nor bluestockings, nor thinking women; too much audacity, culture, intelligence, or character frightens them.
In most novels, as George Eliot observes, it is the dumb, blond heroine who outshines the virile brunette; and in The Mill on the Floss, Maggie tries in vain to reverse the roles; in the end she dies and it is blond Lucy who marries Stephen. In The Last of the Mohicans, vapid Alice wins the hero’s heart and not valiant Cora; in Little Women kindly Jo is only a childhood friend for Laurie; he vows his love to curly-haired and insipid Amy.
To be feminine is to show oneself as weak, futile, passive, and docile. The girl is supposed not only to primp and dress herself up but also to repress her spontaneity and substitute for it the grace and charm she has been taught by her elder sisters. Any self-assertion will take away from her femininity and her seductiveness.”
― The Second Sex
In most novels, as George Eliot observes, it is the dumb, blond heroine who outshines the virile brunette; and in The Mill on the Floss, Maggie tries in vain to reverse the roles; in the end she dies and it is blond Lucy who marries Stephen. In The Last of the Mohicans, vapid Alice wins the hero’s heart and not valiant Cora; in Little Women kindly Jo is only a childhood friend for Laurie; he vows his love to curly-haired and insipid Amy.
To be feminine is to show oneself as weak, futile, passive, and docile. The girl is supposed not only to primp and dress herself up but also to repress her spontaneity and substitute for it the grace and charm she has been taught by her elder sisters. Any self-assertion will take away from her femininity and her seductiveness.”
― The Second Sex
“Here we see that models, despite their reputation for impartiality, reflect goals and ideology. When I removed the possibility of eating Pop-Tarts at every meal, I was imposing my ideology on the meals model. It’s something we do without a second thought. Our own values and desires influence our choices, from the data we choose to collect to the questions we ask. Models are opinions embedded in mathematics.”
― Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
― Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
“Just imagine if police enforced their zero-tolerance strategy in finance. They would arrest people for even the slightest infraction, whether it was chiseling investors on 401ks, providing misleading guidance, or committing petty frauds. Perhaps SWAT teams would descend on Greenwich, Connecticut. They’d go undercover in the taverns around Chicago’s Mercantile Exchange.”
― Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
― Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
“Racism, at the individual level, can be seen as a predictive model whirring away in billions of human minds around the world. It is built from faulty, incomplete, or generalized data. Whether it comes from experience or hearsay, the data indicates that certain types of people have behaved badly. That generates a binary prediction that all people of that race will behave that same way. Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias.”
― Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
― Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
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