| Title: |
Face the Facts: Using Face Averaging to Visualize Gender-by-Race Bias in Facial Analysis Algorithms |
| Authors: |
Owens, Kentrell; Freiburger, Erin; Hutchings, Ryan; Sim, Mattea; Hugenberg, Kurt; Roesner, Franziska; Kohno, Tadayoshi |
| Source: |
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society; Vol. 7 No. 1 (2024): Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24); 1101-1111 ; 3065-8365 |
| Publisher Information: |
Association for the Advancement of Artificial Intelligence |
| Publication Year: |
2024 |
| Collection: |
Association for the Advancement of Artificial Intelligence: AAAI Publications |
| Description: |
We applied techniques from psychology --- typically used to visualize human bias --- to facial analysis systems, providing novel approaches for diagnosing and communicating algorithmic bias. First, we aggregated a diverse corpus of human facial images (N=1492) with self-identified gender and race. We tested four automated gender recognition (AGR) systems and found that some exhibited intersectional gender-by-race biases. Employing a technique developed by psychologists --- face averaging --- we created composite images to visualize these systems' outputs. For example, we visualized what an "average woman" looks like, according to a system's output. Second, we conducted two online experiments wherein participants judged the bias of hypothetical AGR systems. The first experiment involved participants (N=228) from a convenience sample. When depicting the same results in different formats, facial visualizations communicated bias to the same magnitude as statistics. In the second experiment with only Black participants (N=223), facial visualizations communicated bias significantly more than statistics, suggesting that face averages are meaningful for communicating algorithmic bias. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://ojs.aaai.org/index.php/AIES/article/view/31707/33874; https://ojs.aaai.org/index.php/AIES/article/view/31707 |
| DOI: |
10.1609/aies.v7i1.31707 |
| Availability: |
https://ojs.aaai.org/index.php/AIES/article/view/31707; https://doi.org/10.1609/aies.v7i1.31707 |
| Rights: |
Copyright (c) 2024 Association for the Advancement of Artificial Intelligence |
| Accession Number: |
edsbas.5ACEC156 |
| Database: |
BASE |