[4R2-OS-19] OS-19

[4R2-OS-19-03] A Study on Detecting Subtle Biases by Expanding the Log-likelihood into a Frequency Space

〇Yuri Ishitoya¹, Ichiro Kobayashi¹ (1. Ochanomizu University)

Keywords:Gender Bias, Likelihood, Frequency, Anomaly detection

In recent years, approaches that use the likelihood of a language as a means of distinguishing between human and generative model text have attracted a great deal of attention. In this study, we develop a method to detect unconscious bias in human-generated text by referring to the method developed there. We propose a method that extracts frequency components from a series of log-likelihoods of text and extracts features related to the existence of bias by processing in the frequency domain. The proposed method did not exceed the accuracy by the embedding vector. In the future, we would like to verify the performance of the proposed method by adjusting the frequency components that are input.