Wenfan Zhang

Publication Date

Fall 2023

Degree Type

Master's Project

Degree Name

Master of Science in Data Science (MSDS)

Department

Computer Science

First Advisor

Nada Attar

Second Advisor

Reem Al-Baghli

Third Advisor

Wendy Lee

Keywords

ethnicity bias, bias detection, eye-tracking, human-computer interaction (HCI), cognitive load

Abstract

Contemporary Human-Computer Interaction (HCI) research has an increasing emphasis on reducing ethnicity bias. The study presents a new method to explore and reduce biases using detailed experiments. The experimental procedure involves presenting participants with images of ethnically diverse characters across three conditions. The study's results significantly illuminate ethnicity bias in character selection dynamics. Participants exposed to targeted training interventions displayed a significant shift in preferences for characters engaged in intellectual activities. Notably, this shift was influenced by the ethnicity of the characters involved. Interestingly, the eye-tracking data unveiled distinct patterns of cognitive load, characterized by slower response times and greater pupil dilation. This suggests that ethnicity-specific interventions in HCI can effectively reduce ethnicity bias in character selection. These findings have broad implications for areas affected by ethnicity bias, highlighting the importance of targeted training for more equitable and unbiased interactions.

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