Ineffective Automated Content Moderation in Small Language Groups: A Case Study on Facebook and Twitter
February 16, 2021
This AI incident highlights the challenges faced in enforcing violation rules for small language groups such as Balkan languages on platforms like Facebook and Twitter. The alleged cause is a lack of investment in human moderation and design difficulties in AI solutions tailored for these languages. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Preventing such harm requires robust AI governance and safe, secure, and trustworthy AI solutions. Ready to help shape responsible AI? JOIN US
- Alleged deployer
- facebook, twitter
- Alleged developer
- facebook, twitter
- Alleged harmed parties
- facebook-users-of-small-language-groups, twitter-users-of-small-language-groups
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/143
Data source
Incident data is from the AI Incident Database (AIID).
When citing the database as a whole, please use:
McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.
Pre-print on arXiv · Database snapshots & citation guide
We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.