Hierarchical Data Visualization of Gender Difference: Application to Feeling of Temperature

New social problems caused by data bias have been an important issue in recent years. Data bias is sometimes difficult to determine using only quantitative methods, and it requires human decision-making to resolve. Visualization technology can help humans understand data bias and assist with this issue. This paper proposes a visualization method for detecting intersectional bias caused by multiple attributes. The method generates a hierarchical structure by classifying data items according to multiple attributes, and applies a hierarchical data visualization method equipped with band charts. It provides a visualization that gives an overview of the entire data and makes it easy to detect biases in a particular subset of the data. This paper introduces an application to the gender difference in the feeling of temperature with air conditioning systems, and presents the results of participant evaluations to verify the effectiveness of the proposed visualization over baseline implementation.