Detecting and Mitigating Bias in Natural Language Processing

Explore the critical issue of bias in large pre-trained language models (LLMs) through this 50-minute conference talk from the Data Science Festival Summer School. Delve into the sources of bias in uncurated training corpora and their potential for causing societal or individual harm when deployed in commercial settings. Learn about recent methods for measuring and mitigating bias in natural language processing (NLP) and transfer learning techniques. Join Data Scientists Benjamin Ajayi-Obe and David Hopes from Depop as they discuss strategies to address undesirable model behaviors and promote more ethical AI development. Gain valuable insights into creating fairer and more inclusive language models for real-world applications.

Detecting and Mitigating Bias in Natural Language Processing