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Comment by jakear | Hacker News Reader

“In most machine scoring states, any of the randomly selected essays with wide discrepancies between human and machine scores are referred to another human for review”.

And “between 5 to 20 percent” of essays are randomly selected for human review.

So the takeaway is that if you’re one of the 80-95% of (typically black or female) people who the machine scored dramatically lower, but are not selected for human review, your education future is systematically fucked and you have no knowledge of why or how to change it.

Absolutely reprehensible. Anyone involved in the creation or adoption of these systems should be ashamed.

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The thing is, you could be similarly screwed by a biased human whose grading is not checked by a less biased human.

At least the machines offer the following hope: even if unbiased humans are rare among paper-grading teachers, those humans can be used to train the machines, so then bias-free or lower-bias grading becomes more ubiquitous.

Basically, the system has the potential for systematically identifying and reducing systematic bias. A computer program can be retrained much more readily than nation-wide army of humans. Humans can be given a lecture on bias, and then they will just return to their ways.

AI has a lot more potential for bias than humans. It depends on the input data which is likely heavily biased based on other data set results like face detection. It will only amplify any small bias present in the data.

So will humans. With AI, the model and training data are auditable and, if necessary, modifiable. You can't audit a person's life history and you can only really modify a grader by replacing them, and it's much less socially damaging to retrain a model than to fire an employee.

AI certainly has a lot of potential for bias, but claims that AI bias is somehow worse than good old human bias always seem shoddily supported (Note I'm not claiming it's untrue. Just that it's never been shown to my satisfaction, which is not surprising given how quickly AI is changing. It may well be true.)

> but claims that AI bias is somehow worse than good old human bias always seem shoddily supported

Well, AI bias can combine sampling bias with human bias. Like say we train the AI with the output of only 10 human paper graders, all chosen from the same school district.

Due to the sampling bias, that data could create markedly more (or less!) bias than the entire population of human paper-graders.

The resulting AI will ideally mimic those 10 humans, though; it shouldn't show more bias than that group. If those 10 are flaring racists, and grade accordingly, the AI will be the same. (In fact, we hope that it will be the same, if the algorithm actually works in mimicking human grading.)

It's amazing to see how the general opinion of CS people has completely shifted in the last few years from "algorithmic scoring is important in removing the bias from human graders" to the exact opposite.

That was the hope, but all the most effective methods suffer from data collection bias and the studies show that makes them worst than implicitly biased humans.

If we can quantify the bias in the machine, that gives us an opportunity to close the feedback loop and control the bias.

The bias comes from the human-generated training data in the first place; the machine isn't introducing its own. For instance, the machine has no inherent concept of disparaging someone's language because it's from an identifiable inner city dialect. If it picks up that bias, at least it will apply it consistently. When we investigate the machine, the machine will not know that it's being investigated and will not try to conceal its bias from us.

On the other hand, eliminating bias from humans basically means this: producing a new litter of small humans and teaching them better than their predecessors.

If...

It is important but its not ready.

>Anyone involved in the creation or adoption of these systems should be ashamed

That's the problem - there is seemingly no shame these days. People involved "saved time and money", got paid and that's it. "If I didn't do it someone else would" and all of that.

Weapons of Math Destruction talks about this.

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