[HN Gopher] AI Detection Tools Falsely Accuse International Stud...
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       AI Detection Tools Falsely Accuse International Students of
       Cheating
        
       Author : jyunwai
       Score  : 20 points
       Date   : 2023-08-14 21:24 UTC (1 hours ago)
        
 (HTM) web link (themarkup.org)
 (TXT) w3m dump (themarkup.org)
        
       | wilg wrote:
       | Another reason these AI detection tools are snake oil.
        
       | mustafa_pasi wrote:
       | Classifying people using AI is always going to lead to
       | discrimination as long as there are differences between
       | genders/ethnicities/socio-economic backgrounds. This is not
       | preventable and you cannot do anything about it.
        
         | gotoeleven wrote:
         | You can thumb the scale until you get the answer you want.
        
       | godelski wrote:
       | This will always happen and will happen much more as we move
       | forward. People specifically tune their models to sound human.
       | People specifically use detectors to turn models, adversarially.
       | 
       | I keep proclaiming that we live in Goodhart's Hell, and this is
       | yet another great example.
       | 
       | First, you have to remember that metrics are never a complete
       | measure, but they are guides. This doesn't matter if you're
       | cutting lumber or training a ML model. Metrics are __always__
       | proxies. For the vast majority of things you are intending to
       | evaluate, you don't actually have a measurable function to
       | express that. Every fucking measurement has error/noise, and this
       | is why you measure twice and cut once. It is absolutely insane
       | that in the age of advanced algorithms we've just tossed caution
       | to the wind and we're instead doing a half assed measurement and
       | then getting upset when our cuts are bad, blaming the ruler or
       | blaming the saw.
       | 
       | Second, you have to ask how closely your metric aligns with the
       | thing you're actually trying to measure. Even cutting lumber you
       | have to check that your measurement aligns. Accounting for angles
       | and grain. We should be doing the same in algorithms, but with
       | __much__ more care, but we don't for some reason.
       | 
       | For some reason we seem to have this belief that empirical
       | measurements are absolute and generalized. We seem to have this
       | belief that noise is a thing that can be decoupled from the
       | system and precisely removed. We seem to have this belief that
       | measurements are unhackable. What the fuck is going on. This
       | isn't just ML, it is academia, it is bureaucracy, it is the
       | workplace, it is governments, it is economics. So I gotta ask,
       | what the fuck is going on? In the past we treated things as hard,
       | so there was an inherent uncertainty built into mental models but
       | now we treat everything like they are simple to understand and a
       | quick reading or video can make you an expert. We may have never
       | had nuance from non-experts, but now everyone acts like an expert
       | and that math is unbiased and objective. Why do we keep shooting
       | ourselves in the foot and deciding the next best action is to
       | reload the gun?
        
       | rondrabkin wrote:
       | I worked for a big testing company. There are all sorts of
       | cheating and many are easy to detect with the right tools. Maybe
       | this is just the wrong tool.
        
       | noodlesUK wrote:
       | There is (and always has been) a fairly effective way of
       | detecting cheating. Have a conversation with a student. Ask them
       | about some of their recent work. This can be in the form of
       | something more formal like a viva, or it can be a casual chat.
       | 
       | These conversations should not be graded, just flag for problems
       | (of which cheating is just one).
       | 
       | Combining this with verifying the student's ID is about as good
       | as it gets. I think a mandatory 1-on-1 meeting with a member of
       | staff at least once per term is essential, and serves many
       | purposes.
       | 
       | EDIT: I think oftentimes it's known which students are cheating,
       | but it's not easy to do anything about it.
        
         | usea wrote:
         | I've had teachers try this method with me and determined I was
         | cheating when I was not. I don't believe in it.
        
           | JohnFen wrote:
           | This happened to me in secondary school, and I'm _still_ mad
           | about it.
        
         | justrealist wrote:
         | When the student is ESL in the first place (and doing
         | coursework via text), it's pretty hard to determine whether
         | they are cheating or just struggling to communicate verbally.
        
         | pessimizer wrote:
         | Personal attention costs money.
        
           | abeppu wrote:
           | ... but university students (and especially international
           | students) are paying a lot of money.
           | 
           | We're all (understandably) upset if we pay for something much
           | more modest and when it has an issue we're unable to talk to
           | a real human to try to resolve the issue. Imagine paying
           | _many thousands per year_, working on projects and papers,
           | being taught increasingly by adjuncts, struggling to register
           | for all your required classes as administrators admit more
           | students every year (without hiring more faculty), and then
           | when you're accused by a bot of cheating, the response to a
           | suggestion that you should talk with a staff person at least
           | once per term is "oh that's too expensive".
        
         | [deleted]
        
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       (page generated 2023-08-14 23:01 UTC)