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Imagine that LLMs reproduce the biases of their training sets and human data sets are biased against nonstandard speakers with rural accents/dialects/AAVE as le
by AlotOfReading 10mo ago
Imagine that LLMs reproduce the biases of their training sets and human data sets are biased against nonstandard speakers with rural accents/dialects/AAVE as less intelligent. Do you imagine their grade won't be slightly biased when the entire "council" is trained on the same stereotypes?
Appeals aren't a solution either, because students won't appeal (or possibly even notice) a small bias given the variability of all the other factors involved, nor can it be properly adjucated in a dispute.
- makeitdouble 10mo agoI might be given too much credit, but given the tone of the post they're not trying to apply this to some super precise extremely competitive check. If the goal is to assess whether a student properly understood the work they submitted or more generally if they assimilated most concepts of a course, the evaluation can have a bar low enough for let's say 90% of the student to easily pass. That would give enough of margin of error to account for small biases or misunderstandings. I was comparing to mark sheet tests as they're subject to similar issues, like students not properly understanding the wording (and usually the questions and answer have to be worded in pretty twisted ways to properly) or straight checking the wrong lines or boxes. To me this method, and other largely scalable methods, shouldn't be used for precise evaluations, and the teachers proposing it also seem to be aware of these limitations.