Here’s a lesser-known fact that might raise your eyebrows: out of all living birds, 70 percent are chickens and other poultry, and at least five million of the remainder are illegally traded wildlife. Why is this surprising, besides the fact that factory farming is so often hidden from public view? The answer might lie in the tools we use to find information online. A quick image search of the word “birds” will show creatures flying free in their natural habitats, while only a fraction of search results point to the poultry or illegal wildlife trade industries.
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Here’s a lesser-known fact that might raise your eyebrows: out of
all living birds, 70 percent are chickens and other poultry, and at
least five million of the remainder are illegally traded wildlife.
Why is this surprising, besides the fact that factory farming is so
often hidden from public view? The answer might lie in the tools we
use to find information online. A quick image search of the word
“birds” will show creatures flying free in their natural habitats,
while only a fraction of search results point to the poultry or
illegal wildlife trade industries.
New research by digital ethics experts uncovers why this might be
the case, and why machine learning models used to train AI systems
may be at the heart of the problem. The computer models that teach
different kinds of AI to provide images and chatbot replies are not
neutral, it turns out. In fact, the researchers found that these
models either reinforce existing biases about farm animals, or
conceal the connection to factory farming altogether. Either way,
the result is the same — we humans are passing down our prejudices
to these different AI systems, who in turn spread these biases to
our collective unconsciousness like a virus.
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