One of the significant challenges women face in the Natural Language Processing (NLP) field is the presence of gender bias in algorithms. NLP algorithms are often trained on historical data, which can perpetuate and amplify existing biases. This not only affects the representation of women in language models but also impacts the performance of NLP applications in recognizing and processing female-centric content accurately.
- Log in or register to contribute
Contribute to three or more articles across any domain to qualify for the Contributor badge. Please check back tomorrow for updates on your progress.
