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Google fires a researcher who attributes the decision to her complaint of bias in artificial intelligence



Timnit Gebru She was one of Google's leading AI ethics researchers and has been fired. It was she herself who communicated it through your Twitter account blaming the decision on retaliation for an email I sent to other colleagues.



The message in question, which has been viewed by The New York Times, Gebru had expressed his frustration with the company's response to your efforts and those of other colleagues to increase minority hiring and to draw attention to biases in artificial intelligence. "There is no way that more documents or more conversations will achieve anything," he said.






"There is no way that more documents or more conversations will accomplish anything"




He accused Google of not listening




artificial intelligence



The researcher, one of the few black women in her field of work, complained of not being listened to and suggested to the groups of colleagues I send the email to stop writing documents because nothing was achieved with them.



"Your life starts to get worse when you start advocating for underrepresented people. You start making the other leaders upset."it said in the email.



After that, He asked his superiors for changes in working conditions with the threat of leaving the company if these requests were not met. Those responsible for Google to whom he contacted replied that they could not attend to them and, therefore, immediately accepted his resignation from the position.




"Your life starts to get worse when you start advocating for underrepresented people. You start making the other leaders upset."










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In the response they sent, always as shared by the researcher, they told her that certain aspects of the email I had sent reflected behavior that is not consistent with what is expected of a Google directive. A company spokesperson declined to comment to The New York Times about these facts.



Gebru is known for the research she conducted with Joy Buolamwini in which they found that various commercially available facial recognition systems correctly identified the gender of individuals 99% of the time when they were fair-skinned males, but failed when they had to deal with dark-skinned people, with the error rate reaching 35% in the case of women.



Featured Image | Ben Nuttall (CC BY-SA 2.0)