Mathematician Says Big Data Is Causing a ‘Silent Financial Crisis’

Mortgages are very specifically an area where you do. First of all, there is historical data.

Second of all, you can backtest well before 25 years. A couple of weeks ago I wrote a blog post explaining specifically how to make measurements in the presence of delayed reactions - I'm discussing a situation involving sensor networks, literally the same mathematics would work for mortgage default or refinance: https://www.chrisstucchio.com/blog/2016/delayed_reactions.ht...

Third, mortgage lenders can often backtest alternate decisions because there are pretty straightforward relationships between decisions. Some of them are even mechanistic, e.g. refinance_risk(interest_rate) and default_prob(interest_rate) are monotonically increasing.

You seem to think we are living in the exact specific dark age necessary to make your morality play poignant and relevant. That's about as silly as Star Trek landing on all sorts of alien planets, each one designed to highlight one specific social issue from USA 1966.

This seems to be what he is referring to.

An interesting similar practical application was differential sentencing for drug dealing outside versus drug dealing inside. White and black Americans use illegal drugs at essentially the same rate, with white Americans pulling ahead in several categories historically. Suburban Americans more often deal drugs inside houses, while urban Americans are more likely to deal drugs outside than suburban Americans. Due in part to redlining practices, black people are more likely to live in urban areas and white people in suburban areas. So just send a bunch of cops to urban neighborhoods and you'll catch plenty of drug dealers and give them higher sentences, while letting all the white drug dealers go and giving them lower sentences if their behavior is egregious enough that you've got to arrest them.

This example doesn't even get into differential treatment for white-collar crime (scamming your investors) vs stealing meat from a grocery store. All those articles we saw on HN about scamming your employees and investors? MotionLoft, 1for.one? Those folks are "just doing business" and most commenters seemed to implicitly condone the scamming behavior -- sure it's unfortunate that it happens but you know business -- while no one here would condone stealing meat from a grocery store, and the meat-lifter would be more likely to get caught since there are cops at the doors of those grocery stores. Do we need cops in the startup scene if scams are happening, though?

The bad thing is not targeting "needs," as above, it's targeting perceived needs or exploiting differences in a way that contributes to injustice. I can see how you might not care about targeted advertising that allows you to ignore the lives of the poor -- that's fairly normal and in the US we really rely on it for political stability -- but ads that only show high-cost high-interest low-placement educational options for poor people push out ads from your local community college, targeted ads contribute to the political polarization we're seeing, targeted ads show higher-paying jobs to men than women regardless of qualifications. There is information asymmetry in a lot of these situations and in several ways: if the poor person trying to look up education doesn't already know about the low-cost legit CC and the information is buried after a page of for-profit ads, they may not understand all their options, and just as bad, we who earn a bit more may not know that's happening, because when we look up education to see if our teen could take multivariable calc nearby, we will get the CC right at the top of the page and the for-profit schools, not targeting us, won't show up! And then we can grumble with a clean conscience that poor people are so dumb they can't even look at the first search result...

However, for something like a credit approval algorithm, there is a valid issue of centralisation - if there are problems with the algorithm, a large number of people will be affected negatively by the same algorithm. While humans are biased and imperfect, you can find some who are less biased, or call their manager and see if they can justify themselves, etc. I can imagine situations where it's hard to escape or find recourse when you're faced with an algorithm that's put you in an unfavourable situation.

I'm hoping that, given enough data to train on and enough validation to make sure they are working optimally, this will not really be a big issue, but I can understand if some people are concerned about that aspect.

Yes, but are there any positive effects of police presence? Does this outweight the negative effects?

> The author is saying (assuming you think like them) that ignorance of suffering is a bad thing

So seeing poorly targeted ads tells you that people are suffering? It is the role of corporations to inform you that there are people who have different needs than you?