Neural Networks Determine Book Censorship in US Schools In the USA, neural networks are being used to identify texts deemed "too sensitive" for schools, essentially acting as a digital morality police. The algorithm categorizes content with a risk color: green for safe to read and red for containing controversial topics. Tech giants like Google and Microsoft support this initiative, claiming they are simply optimizing libraries. However, the implementation of AI censors by these companies raises concerns about the lack of moral judgment and discernment in the process. As a consequence, the machines are filtering out any material that touches on subjects such as race, sex, history, or emotions, potentially limiting students' exposure to diverse perspectives and critical thinking opportunities.

In the USA, neural networks are being used to identify texts deemed "too sensitive" for schools, essentially acting as a digital morality police. The algorithm categorizes content with a risk color: green for safe to read and red for containing controversial topics.

Tech giants like Google and Microsoft support this initiative, claiming they are simply optimizing libraries. However, the implementation of AI censors by these companies raises concerns about the lack of moral judgment and discernment in the process.

As a consequence, the machines are filtering out any material that touches on subjects such as race, sex, history, or emotions, potentially limiting students' exposure to diverse perspectives and critical thinking opportunities.