Facebook Allegedly Failed to Police Anti-Rohingya Hate Speech Content That Contributed to Violence in Myanmar
Description
Facebook allegedly did not adequately remove anti-Rohingya hate speech, some of which was extremely violent and dehumanizing, on its platform, contributing to the violence faced by Rohingya communities in Myanmar.
Actors
Alleged Deployers
- meta
Alleged Developers
- meta
Harmed Parties
- rohingya-people
- rohingya-facebook-users
- myanmar-public
- facebook-users-in-myanmar
- burmese-speaking-facebook-users
Classification
Risk Domain
Discrimination & ToxicityRisk Subdomain
1.2. Exposure to toxic content
MIT Risk Profile
Topics
Source
Related Incidents (Discrimination & Toxicity)
View all →Research Papers on This Topic
View all →Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Explaining Explanations: An Overview of Interpretability of Machine Learning
RISE: Randomized Input Sampling for Explanation of Black-box Models
Mitigation Tools
Tools related to this risk domain, via shared responsible AI topics.
Open Source
View all →AIF360
Trusted-AI/AIF360
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
⭐ 2,872
fairlearn
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
⭐ 2,199
responsible-ai-toolbox
microsoft/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.