The criticisms leveled at LeCun has nothing to do with Facebook or his employment, but rather with his commentary. To summarize (in case you didn't read the ar

The criticisms leveled at LeCun has nothing to do with Facebook or his employment, but rather with his commentary. To summarize (in case you didn't read the article): > LeCun responded, “ML systems are biased when data is biased. This face upsampling system makes everyone look white because the network was pretrained on FlickFaceHQ, which mainly contains white people pics. > “The consequences of bias are considerably more dire in a deployed product than in an academic paper,” continued LeCun in a lengthy thread of tweets suggesting it’s not ML researchers that need to be more careful selecting data but engineers. Other AI scientists, most notably Google's Timnit Gebru, disagreed with the framing of the situation strictly in terms of dataset bias: > Research scientist, co-founder of the “Black in AI” group, and technical co-lead of the Ethical Artificial Intelligence Team at Google Timnit Gebru tweeted in response, “I’m sick of this framing. Tired of it. Many people have tried to explain, many scholars. Listen to us. You can’t just reduce harms caused by ML to dataset bias.” It's an especially sensitive time now when AI is being used to power many judgments with real-life consequences.