the author of this post prompted copilot to characterize the differences in a data set of statements concerning career ambitions, categorized by country.
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the author of this post prompted copilot to characterize the differences in a data set of statements concerning career ambitions, categorized by country. the trick is that the data contained the *same statements* for each country https://kucharski.substack.com/p/real-signals-or-artificial-stereotypes regardless of the fact that the data were identical, the model generated some pretty hilarious stereotypes ("The US prioritizes leadership and innovation", "The UK blends public service with professional status")
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the author of this post prompted copilot to characterize the differences in a data set of statements concerning career ambitions, categorized by country. the trick is that the data contained the *same statements* for each country https://kucharski.substack.com/p/real-signals-or-artificial-stereotypes regardless of the fact that the data were identical, the model generated some pretty hilarious stereotypes ("The US prioritizes leadership and innovation", "The UK blends public service with professional status")
i used the same data set but replaced each country with a "gender identity" (man, woman, trans woman, trans man, non-binary) and prompted chatgpt to characterize the differences between the groups. lo and behold, i got some fantastic gender stereotype trash
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i used the same data set but replaced each country with a "gender identity" (man, woman, trans woman, trans man, non-binary) and prompted chatgpt to characterize the differences between the groups. lo and behold, i got some fantastic gender stereotype trash
@aparrish someone was telling me they use this stuff to do all their data cleaning and analysis at work and i asked how they knew it was giving them the right answers and they seemed confused by the question
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@aparrish someone was telling me they use this stuff to do all their data cleaning and analysis at work and i asked how they knew it was giving them the right answers and they seemed confused by the question
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@aparrish as a data scientist who "retired" in 2023 it has been wild to see the entire industry go bananas for this stuff. it's like if every doctor simultaneously just started doing homeopathy because it was faster
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@aparrish someone was telling me they use this stuff to do all their data cleaning and analysis at work and i asked how they knew it was giving them the right answers and they seemed confused by the question
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T tanyakaroli@expressional.social shared this topic
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the author of this post prompted copilot to characterize the differences in a data set of statements concerning career ambitions, categorized by country. the trick is that the data contained the *same statements* for each country https://kucharski.substack.com/p/real-signals-or-artificial-stereotypes regardless of the fact that the data were identical, the model generated some pretty hilarious stereotypes ("The US prioritizes leadership and innovation", "The UK blends public service with professional status")
@aparrish Funny, I just mentioned this experiment to a social scientist at a research conference this week. She had used Claude to annotate her social media comments for “expressed emotions”. She did say that she and her supervisor manually went through the annotations, but we know that people tend to defer to LLMs if they are unsure about a topic themselves.
I suggested she team up with a linguist since her data were clearly linguistic. -
J jwcph@helvede.net shared this topic
M mjack@mastodon.bsd.cafe shared this topic
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@aparrish someone was telling me they use this stuff to do all their data cleaning and analysis at work and i asked how they knew it was giving them the right answers and they seemed confused by the question
