Machine learning is helping auto lenders make better decisions

This insinuates that car lenders are racist.

I am truly curious of what the business model of denying minorities a car loan is?

I mean, long ago, I guess I can at least process the ugly mindset of "we don't want 'them' as our neighbor", but I can not come up with any logical or illogical reasonable reason why a business person would intentionally lower their income just to be racist.

Literally no one would know that you made a loan to a minority, so that can't be it.

Is the insinuation that the loan officer might "get a feeling" that a person might not repay a loan based on the race?

If that were the case, at least in the financial sector, it would seem that another financing institution would have noticed this inefficiency long before 2019 and gobbled up all of that free money on the table.

I also wonder if the higher interest rates of credit cards accounts for higher approval rates.

How do auto loans and credit card approvals differ? Surely both have some human element to it still?

Maybe the sums involved are different, so giving loans is more risky than approving credit cards?

Also, incentives might be different. Maybe credit card companies want "bad" borrowers, because they make the most money if people go into high debts on credit cards? On the other hand for normal loans there is no such incentive, because the interest rates are fixed and much lower.

This is where things get complex with ML. Any dimension that is correlated with the protected categories can potentially get you into a biased and discriminatory outcome. Because embedding are not necessarily transparent, it’s hard to evaluate what the model attaches to.

The most accessible example is anything to do with fine grained location, such as zip+4. Because people tend to cluster, zip+4 has been identified as a close proxy for race and other protected classes.

When we are talking ML-derived decisions and the nuanced approach, we have to be cognizant of the fact that certain repayment patterns and behaviors could also be strongly correlated to a protected class.

I'm not seeing any evidence that the ML approach doesn't have the same or similar biases. I'm sure the results are different but we can't say they're better just because a computer generates them. In fact, we've seen many times that ML may often tend to give implicit biases even more emphasis, because in an attempt to create "objective" measures against which to judge a dataset, ML creators inject their own biases, or rely on data which is itself biased in its collection or categorization. (So, for example, if zip code is a major indicator in the ultimate ML algorithm, it may be completely "objective", while still reinforcing biases against minority applicants, simply because of how wildly self-segregated many US cities are, or because of self-reinforcing effects of systemic bias in other, human-controlled parts of the economy.)

Geography may be a factor.

You might reasonably apply by mail for a credit card from an out-of-state bank, but a lot of auto lending revolves around a purchase made in a nearby dealer.

If the dealers in minority-dominant communities tend to be more aggressive about lending qualifications, that could explain it all neatly.

Though, my blind guess would be that you'd see more "buy-here/pay-here" and "no-credit-required-but-the-loan-is-at-34-percent-interest" dealers in minority neighbourhoods (skewing poorer), so approval rates would likely be at par or higher. Or are they qualifying "approval rates" with "at sane market prices?"

If you make bad decisions by offering loans at too low a rate, you will lose money on average and eventually go bankrupt.

If you make bad decisions by offering loans at too high a rate because you have high overheads or you’re cautious or your actuaries are wrong or you’re racist then people won’t take out your loans because they get a better deal elsewhere or the loan is too expensive so you lose money by not profiting from making the loan.

Now there’s a balance here: you want to be competitively priced so people will take out your loans but you also want higher margins so you can make more money for investors or expand or make less money in other places to attract customers.

I think in this case “make better decisions” means either morally better (ie less racist), or better in the sense of “make better deals to win more customers to make more money”

wouldnt a system that is "blind" in the sense that it has no prejudice be free to use those (anything) as features? isnt the whole point of the NN to get loans repaid?

Oddly, prescient.

The Dune trilogy / quadrilogy whatever yes?

The prescription against thinking machines, along with similar plot devices (shields promoting hand-to-hand combat, the defacto ban on atomic weapons) allowed FH to construct a quasi-medieval SF setting.

I wouldn’t take mystical pronouncements in fictional settings intended to advance a plot too seriously.

After a quick scan, I only saw this used once. Does it really bother you that much?

That said, the ellipsis construction is used in an attempt to make text feel more like casual conversation — in this case, something like drawing out the preceding word in a questioning manner or a maybe a pregnant pause. This is a very standard device used in the register that Matt Levine uses for his column, and I imagine that most people find that these casual devices he uses makes his column more accessible for a wider audience.