Row 69450

Row ID: 69450 | Dataset Entry | Axioma AXP Content Repository

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I get what you're arguing, but

>AI doesn't have tact or bias

is just flat out wrong. Its biased towards whatever is in the training data - if your training data is 50% mein kampf, its going to have some bias. And while the size of the datasets used helps normalize this, bias isn't evenly distributed (e.g. theres not an equal amount of counter-stereotypes that say "white people love watermelon")

If the data that was being used to train them was guaranteed to be factual, then maybe youre right - the stereotypes would just be a reflection of that dataset. But the dataset is just the internet

But all of these are technical issues that ARE being worked on by white people who don't want racist models - I dont like the insinuation either that because they're white, they don't care or are incapable of prioritizing them

I mean, remember when Google released their model that tried *too* hard not to be racist, leading to hilarity? That's because Google knows the model has bias and is trying to account for it with prompting

FieldValue
text I get what you're arguing, but >AI doesn't have tact or bias is just flat out wrong. Its biased towards whatever is in the training data - if your training data is 50% mein kampf, its going to have some bias. And while the size of the datasets used helps normalize this, bias isn't evenly distributed (e.g. theres not an equal amount of counter-stereotypes that say "white people love watermelon") If the data that was being used to train them was guaranteed to be factual, then maybe youre right…
label r/technology
dataType comment
communityName r/technology
datetime 2024-05-23
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Raw Record

{
  "text": "I get what you're arguing, but \n\n>AI doesn't have tact or bias\n\nis just flat out wrong. Its biased towards whatever is in the training data - if your training data is 50% mein kampf, its going to have some bias. And while the size of the datasets used helps normalize this, bias isn't evenly distributed (e.g. theres not an equal amount of counter-stereotypes that say \"white people love watermelon\")\n\nIf the data that was being used to train them was guaranteed to be factual, then maybe youre right - the stereotypes would just be a reflection of that dataset. But the dataset is just the internet\n\nBut all of these are technical issues that ARE being worked on by white people who don't want racist models - I dont like the insinuation either that because they're white, they don't care or are incapable of prioritizing them\n\nI mean, remember when Google released their model that tried *too* hard not to be racist, leading to hilarity? That's because Google knows the model has bias and is trying to account for it with prompting",
  "label": "r/technology",
  "dataType": "comment",
  "communityName": "r/technology",
  "datetime": "2024-05-23",
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}
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