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Comment by d--b | Hacker News Reader

Nice to see that the debate has reached the ears of the main people working on this field.

What is important to note here is that we need to tweak the mathematical model to the culture we want to achieve. In other words, the objective function of the optimization problem needs not only match the current state of the world, and provide an hindsight in one's own economic interests, it also needs to take into account the culture that we want to reflect, and influence. Otherwise, these statistics are just a giant status quo amplifier.

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I'm not sure that's clear. There are actually two ways to achieve the outcome we want; tweaking the model or changing the inputs.

What I mean, say the model identifies that a certain group has a greater risk due to systemic problems. If you change something about the group, you can change the calculated risk without changing the model. And this may very well be a better way to achieve the outcome you want.

Specifically, by preventing insurance companies from using a more accurate mode, what you're demanding is that the random people who happen to have taken the same insurance packages but are not part of the group should make an extra contribution to fix these systemic problems.

But why them? Shouldn't we all contribute instead, hopefully using a fair system for assessing how much should each pay?

Instead of tweaking the model, you can change the inputs by providing a state-backed guarantee to the underprivileged groups. Isn't it more fair overall?

No I don't think I expressed myself clearly. The problem is that machine learning learns from biased data. The input data is the total US population, which has wealthier groups than others. Because of the way we train machine learning (using all the data we have), the bias that is in the original data gets transfered to the logic.

Specifically, a machine learning will produce different numbers for 2 individuals with the exact same characteristics except the race. And that is the problem that needs to be addressed.

Let's put it in another context. Let's say I'm a white athlete, and I'm very good at running the 100m race. Actually I run just as fast as a black person who is my main rival. Now if someone has to select one of us to go to the Olympics, they should toss a coin to decide who goes. If you use a ML algorithm, it would absolutely send the black person, because no white people has won a 100m race in the last 20 olympics. That's the kind of bias ML does and that needs to be addressed.

Tweaking the models to lead towards the world we'd like to live in seems like a noble goal, but I wonder how you should incentivize this. If discriminatory models provide higher profits, the idealistic models won't be used.

> What is important to note here is that we need to tweak the mathematical model to the culture we want to achieve

Good point. Systems should work for people, not the other way round.

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