Attacking discrimination with smarter machine learning
Well. At the end of the day, the companies will pick thresholds and rates to maximize their profits, based upon the data that they have available. They don&#x27;t know every detail of our personal lives -- which would also be kind of unsettling -- so they have to resort to a simplified picture. Simplifications are always prejudiced at the individual level but if the prejudice is reflected in the numbers, it&#x27;s balanced. It may not feel perfectly just but at least it&#x27;s more just than an imbalanced prejudice.<p>A real life example: In many (all?) countries, it&#x27;s common to pay more for your car insurance if you are young, if you are male, or if you recently acquired your driver&#x27;s license. One might frame this as a prejudice towards young, male drivers as being reckless. But statistically they are just that, so the prejudice is balanced (within a reasonable tolerance). It&#x27;s unfair to the careful young, male driver, but well, life is not always fair.