Semester Award Granted
Spring 2025
Submission Date
May 2025
Document Type
Thesis
Degree Name
Master of Arts (MA)
Thesis/Dissertation Advisor [Chair]
Aaron Veenstra
Abstract
Algorithmic processing and classification of racism-related content have become increasingly prevalent across Meta's platforms. While these algorithms aim to create safer online environments, their effectiveness and impact on fairness remain understudied. This research examines how Meta's algorithmic processes for classifying racist content affect fairness, bias, and social justice across Facebook, Instagram, and WhatsApp.
Using qualitative research methodology, the study conducted focus groups with diverse platform users to understand their experiences with content moderation. The research also analyzed theoretical frameworks related to algorithmic bias, fairness metrics, and social justice in digital spaces.
Key findings revealed significant variations in algorithmic effectiveness across different languages and cultural contexts, with implications for fairness and user experience. The study identified patterns in false positives and negatives, transparency issues, and challenges in handling intersectional content.
These findings will add to the increasing research base on algorithmic fairness and provide recommendations for improving content moderation systems. The findings offer valuable insights for technology companies, policymakers, and civil rights advocates working to create more equitable digital spaces.
Recommended Citation
Foster, Shanique, "META’S AI BIAS TO WHAT EXTENT DO META'S ALGORITHMIC PROCESSES FOR CLASSIFYING AND PROCESSING INFORMATION RELATED TO ACTS OF RACISM IMPACT FAIRNESS, BIAS, AND SOCIAL JUSTICE ACROSS ITS PLATFORMS?" (2025). Electronic Theses and Dissertations. 72.
https://digitalcommons.fau.edu/etd_general/72