You realize you're advocating totalitarianism, right?
We detached this subthread from https://news.ycombinator.com/item?id=13005244 and marked it off-topic.
However, there is a problem because the population of the US is not uniform. Because of history, some races were less wealthy than others. So far, this makes sense I suppose.
Now, talking about machine learning. The big specificity of machine learning (when it is implemented with neural nets) is that it is trained on data only. The machine doesn't know about biology, and it doesn't know about history. Therefore, the model sort of converges to a place where effectively it "believes" from the data it has seen that there is a correlation between race and ability to repay a loan. So basically, the historical bias that is in the data translates into a logical bias. And so, because of history, you're less likely to get a loan if you're a black person than if you're a white person. That is what I call "amplifying the status quo"
Now, as we live in a society that holds equality of chances as one of its core values, I think it is good that people who work in machine learning look at these bias, and have a criticism towards the results of the algorithms in terms of societal impacts, and not only in terms of economical gain.
This has nothing to do with totalitarianism. Yes it is not libertarian, but we don't live in a libertarian world.
Figure 7 of the underlying paper we are discussing shows exactly that - a white/asian person with a 700 FICO score has about a 10% chance of defaulting, while a black person with a 700 FICO has about a 15% chance of default. (Those numbers are eyeballed from the graph, might not be that accurate.)
I can't cite all the information I have on this, but I've found a blog post which analyzes similar data and derives similar relationships in publicly available data:
https://randomcriticalanalysis.wordpress.com/2015/11/22/on-t...
Being progressive for the sake of "going forward" sounds like misguided idealism. What do we do when progressive computer models lead us down a harmful path, do we simply play the typical liberal blame game and point fingers everywhere else while digging our head into sand?
This isn't even remotely far-fetched. If you're a rural working class person, it's not at all hard to see how machine learning algorithms could adversely affect decisions about your insurance rates, loan acceptances, sentencing, the frequency with which your homes and businesses are inspected, and so on.
The idea that "discrimination" is somehow a progressive bugbear is --- well, I don't know how better to say this --- deeply stupid. Swirling eyes-within-eyes sticking out from blobs of fur inverted machine learning stupid.
What I'm trying to say is correcting discrimination under the guise that it's unfair to certain sections of society is chasing your own tail out of boredom -- what's the point? To improve society, or improve usefulness of machine learning-derived data to make radical (but necessary) decisions? Do we maintain our objectivity and move away from "improving society" when it's found to be counter-productive, or do we keep going forward with improving society and hope things get better?
Trying to "break out of the status quo" to produce more a accurate representation of the world sounds great, I'm totally all for removing human biases, but my concern is that it'll be handled badly to mistakenly attempt to benefit certain classes of society (eg: repeat of subprime mortgage crises from 07-09 but much more difficult to diagnose).
Think of it in terms of survival bias. ML models that discriminate against the well-educated urban workers that design and deploy them are not going to make it to production if they obviously discriminate against their own designers. Those developers and "data scientists" are not going to notice if those same models happen to deny college admission to kids who took a year off after graduating to work at a factory to help pay for their siblings room and board.
How much "discrimination" will be good enough in the end? I'll tell you what the endgame is: no discrimination at all, everybody gets a loan. So, in other words, no loans at all because nobody would conduct business in those conditions.
Second, there's no political orientation I can think of that is entirely comfortable with the status quo. Conservatives believe themselves to be discriminated against, just in a different set of circumstances. The underlying problem with ML is conceptual, not political.