This is my first time learning that AI-graded essays are a thing. Am I the only one who thinks that's insane? I feel like you'd probably have to have an AGI to meaningfully evaluate an essay.
A thing you quickly find if you try to download off-the-shelf NLP tools and apply them to anything is how little is reliable at all, unless you can constrain the domain. Even basic topic identification only works with low error rates when constrained to something like NYT stories, or PubMed abstracts, not arbitrary text by arbitrary writers. And I would bet ETS is using worse tech than research state-of-the-art.
I still agree that automatic essay grading is beyond the reach of SOTA NLP models today, but youmake it sound like virtually nothing can be done in a production-grade manner that solves real world unconstrained NLP problems. This is manifestly false.
I'm not aware of any meta-analyses myself. I have been keeping up with the ASAP competition and various attempts to improve on the initial systems for a number of years. The two papers I believe are having the most success are [1] and [2]. [3] seems promising for balancing the opposing forces of high accuracy for true positives and the risk of false positives via adversarially crafted inputs.
I'm also vaguely aware of research happening around extracting features from neural nets. I'd love to be able to help students understand why the system is predicting a particular score.
[1] https://www.aclweb.org/anthology/D16-1193 [2] https://arxiv.org/pdf/1606.04289.pdf [3] https://arxiv.org/pdf/1804.06898.pdf
People making big decisions with a lot of money around computing know nothing about it and are marks for con-artists. Think big consulting firms selling to senior public servants in washington. "For a successful technology reality must take precedence of public relations." But reality just gets in the way when conning a mark for a successful snake oil sale, right?
Call it out, publically, cite your credentials. Encourage colleagues, your competition and everyone with a clue to pour scorn on whoever is selling this evil, toxic waste as drinkable.
One major problem with algorithmic approaches, whether automated or not, is that they become the definition of good in the context and therefore become something that cannot be argued against. And of course it makes 'teaching to the test' an even more likely outcome.
If I were a conspiracy theorist I'd attribute this to wanting a dumbed down population. Unfortunately I think it is probably the other way round, the population is already dumbed down and a belief in AI unicorns is the result.
As Aristotle said to Alexander: 'There is no royal road to geometry', and so it is with education; it's hard work for both the student and the educator and no amount of AI/ML/algorithmic snake oil will change that without also changing the meaning of the word education.
To emphasize, this was 16 years ago.
So the reason this isn't the case, is because there are very simple metrics that tend to highly correlate with essay quality. It doesn't mean the grading-bot is actually evaluating essay quality. It's just looking for properties that are statistically associated with good essays. Remember, at the end of the day as long as the bot's ranking is close enough to the human grader's ranking, nobody really cares about the internal logic.
A very straightforward example is spelling mistakes. People who make spelling mistakes aren't necessarily bad writers. And vice versa, there may be great speller who can't write for shit. But by and large the people who spell poorly also tend to write poorly. Easily detectable grammatical issues, like misplaced modifiers, subject verb disagreement, or inconsistent tense, are also correlated indicators.
A very simple metric is essay length. Especially if its a timed exam. Good writers tend to have verbal fluidity, with words easily flowing to paper. They don't struggle converting thoughts too sentences. So they tend to end up with the most words written down within a fixed time period. By and large the longer a timed essay is, the more likely that its actual quality is high.
Grading bots basically rely on these statistical relationships. They're not measuring anything intrinsic to good writing. But at the end of the day, their student rankings are usually pretty close to that of a typical human grader. In some cases the bot will have a closer ranking to a random human grader, than two random human graders will have to each other.
The biggest flaw here is Goodwin's law. When the test takers become aware of the kludges that the bots use, they can exploit it. For example just dump a bunch of verbal diarrhea with as many correctly spelled words as possible. But even then it doesn't really hurt the bot's ranking accuracy too much. Because the kids who do the most test-prep and learn all the tips and tricks, are usually high-achievers who do well on essays anyway.
This is related to current fairness-in-AI discussions. In many cases the basic problem is ML systems leverage correlations for making causal decisions. Here, there is a huge ethical difference between scoring a person based on "is this a good essay" and "do the features of this essay correlate with features of good essays". Just like there is a huge fairness and discrimination difference between "is this person qualified for a loan" and "do the features of this person correlate with features of people who qualify for loans" (algorithmic redlining). Your last sentence has a big discrimination/fairness issue also, since you are testing even more for parental income and parental free time.
Thanks for catching the mistake.
Anyway, yeah, not really a correlation.
>Remember, at the end of the day as long as the bot's ranking is close enough to the human grader's ranking, nobody really cares about the internal logic.
This isn't true at all. Imagine you got a B or C on an essay that a human would have given an A to because you wrote it concisely and in plain language, or because you used language that's statistically correlated with being black. Does the fact that this is rare console you? "Sorry, but it's usually very close to the human grader's ranking." Close enough isn't good enough when you get the short end of the stick. "Sorry, you aren't going to get to go to the college you wanted because you use language statistically correlated with poor writing." Or just because you're different, so the statistical correlation doesn't apply to you, you filthy outlier. Just because it's a rare event doesn't make it okay.
In adulthood, this is like hiring or firing for work statistically correlated with good work. Remember when amazon rolled out the resume scorer? [0] Sure it was biased towards women, but it was close enough to human scores, so who cares about the internal logic?
>Grading bots basically rely on these statistical relationships. They're not measuring anything intrinsic to good writing.
At the end of the day, our goal here is to measure good writing. If the bots aren't measuring anything intrinsic to good writing, we shouldn't use them.
https://www.reuters.com/article/us-amazon-com-jobs-automatio...
You could ask a student to write an essay taking a firm opinion on some subject, and they could change standpoint every paragraph and there's no way these systems would know.
If I was a student I would be extremely offended at people wasting my time like this.
Maybe nobody really makes a big deal about it because it is pretty much irrelevant anywah. Applicants provide a letter of intent that the grad dept people can, y'know, actually read for themselves, so I think unless you totally bombed the writing section nobody cared.
Like this kind of thing should be cool, not insane. I mean wasn't it cool in your AI class when you learned that DFS could play Mario if you structured the search space right?
It took me much longer to pass my English language O level (exam taken at 16).
It is fine to play with "cool" techniques when you are doing consequence free stuff like playing Mario. When you are creating systems that have significant and long term effects of people's lives a different standard applies.
* 5% - cool, we could make a company that grades essays
* 15% - cool, we could make a company that grades essays and sell our source code to the test-prep industry
* 80% - fascinating, it sounds like the exam designers need to reevaluate what they are trying to measure with essay questions
And if you succeed you will simply be measuring an uninteresting but manageable subset of the problem which will then become in some people's eyes the definition of the problem.
Education is supposed to be about teaching people to think, to give them the tools with which to do it, to be able to evaluate, criticise, invent, etc.
I'd find it highly interesting to see what kind of result I'd get using an automated system.
Why?
Because, I once asked a teacher (also an examiner) why I got good grades above the others, and the answer surprised me: my answers were generally unique /refreshingly different, to the point/ not too long and easy to read.
I suspect with this new system, I'd be an average student. It'd also be interesting to find out, several years down the road, if the automated system could be gamed at all -- I suspect it could, and teachers would help students 'maximise' their scores as a result of that.
You know, those average writers like Hemingway /s
In fact, throughout the article I kept being surprised by the idea that long is good. When writing, I tend to prefer being brief.
That only really shows that the humans they're training on are terrible at grading essays.
GIGO is our God now.
Yeah, it's cool, but what about your savings account?
Narrowly for grammar however - is even that a good thing? It probably helps scale grammar help to more students, but if those tools became ubiquitous in grading and editing then unique voices would just disappear and a lot of potentially “great writers” might choose different careers because the machines don’t like them
The fact that it's being implemented in society is insane because anyone who is paying attention to the state of AI today already knows how it will go wrong: without reading the article I already guessed that it systematically discriminated against certain demographics. Which was in fact what the article claimed.
It's interesting that it's possible to predict what the scorer would decide, but the moment you actually implement it is when all of the known problems become relevant, and the intellectual wonder must take a backseat to the human problems.