Saturday, November 24, 2018

Should credit scores be used for determining residency?

It's both exhilarating and frustrating when you see the warnings in papers you write play out in practice. Case in point, the proposal by DHS to use credit scores to ascertain whether someone should be granted legal residence.

Josh Lauer at Slate does a nice analysis of the proposal and I'll extract some relevant bits for commentary. First up: what does the proposal call for? (emphasis mine)
The new rule, contained in a proposal signed by DHS Secretary Kirstjen Nielsen, is designed to help immigration officers identify applicants likely to become a “public charge”—that is, a person primarily dependent on government assistance for food, housing, or medical care. According to the proposal, credit scores and other financial records (including credit reports, the comprehensive individual files from which credit scores are generated) would be reviewed to predict an applicant’s chances of “self-sufficiency.”
So what's the problem with this? What we're seeing is an example of the portability trap (from our upcoming FAT* paper). Specifically, scores designed in a different context (for deciding who to give loans to) are being used in this context (to determine self-sufficiency). Why is this a problem?
Unfortunately, this is not what traditional credit scores measure. They are specialized algorithms designed for one purpose: to predict future bill-paying delinquencies, for any reason. This includes late payments or defaults caused by insurmountable medical debts, job loss, and divorce—three leading causes of personal bankruptcy—as well as overspending and poor money management.
That is, the reason the portability trap is a problem is because you're using one predictor to train another system. And if you're trying to make any estimations about the validity of the resulting process, then you have to know whether the thing you're observing (in this case the credit score) has any relation to the thing you're trying to observe (the construct of "self-sufficiency"). And this is something we harp on a lot in our paper on axiomatic considerations of fairness (and ML in general)

And in this case there's a clear disconnect:
Credit scores do not predict whether an individual will become a public charge. And they do not predict financial self-sufficiency. They are only useful in this context if one believes credit scores reveal something about a person’s character. In other words, if one believes that people with low credit scores are moochers and malingerers. Given the Trump administration’s hostility toward (brown-skinned) immigrants, this conflation of credit scores and morality is not surprising.
And this is a core defining principle of our work: that beliefs about the world control how we choose our representations and learning procedures: the procedures cannot be justified except in the context of the beliefs that underpin them. 

I think that if you read anything I've written, it will be clear where I stand on the normative question of whether this is a good idea (tl;dr: NOT). But as a researcher, it's important to lay out a principled reason for why, and this sadly merely confirms that our work is on the right track.


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