Detecting Bias in Refugee Perception using Face Swapping: An Empirical Eye-Tracking Study
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
2024 IEEE 4th International Conference on Human-Machine Systems, ICHMS 2024
DOI
10.1109/ICHMS59971.2024.10555693
Abstract
Refugee populations across various regions face numerous challenges, including ethnocentric biases. In particular, numerous articles have highlighted the struggles Syrian refugees face in some countries due to Western media bias. In contrast, the media coverage of the Ukrainian crisis has been demonstrated to be different, with fewer restrictions from certain governments. This study explores the potential of incorporating empirical research in the field of human-computer interaction to create stimuli employing images related to refugees. Its primary objective is to assess and quantify the presence of bias among both Ukrainian and Syrian refugees. To achieve this, we utilized eye-tracking equipment to examine participants' decision-making in specific scenarios and analyzed the resulting pupil size data to gain insights into their attitudes toward refugees from these backgrounds. We utilized original images of refugees from both countries and Ordinary Procrustes Analysis (OPA) to manipulate other images through face swapping before or during tasks. Our study examined whether participants demonstrated biases towards refugees observed in the media and if face swapping influenced their decisions. The results showed that participants donated more when presented with authentic information about real individuals and did not display bias towards a specific ethnicity. We found a positive correlation between pupil size and donation amount when participants viewed real refugee images, but no correlation when the face was swapped before or during the task. This study provides insight into measuring biases and encourages thoughtful interaction with images of refugees as a stimulus
Keywords
Bias, Donation, Eye-Tracking, Face Swapping, Ordinary Procrustes Analysis, Pupil Size, Reaction Time, Refugees
Department
Computer Science
Recommended Citation
Reem Albaghli, Akshay Gurnaney, and Nada Attar. "Detecting Bias in Refugee Perception using Face Swapping: An Empirical Eye-Tracking Study" 2024 IEEE 4th International Conference on Human-Machine Systems, ICHMS 2024 (2024). https://doi.org/10.1109/ICHMS59971.2024.10555693
