Facebook's Algorithmic Bias: Impact on Political Campaign Reach and Polarization
July 10, 2019
Exploring Facebook's political ad delivery algorithms that allegedly biased user reach based on inferred political alignment, potentially reinforcing political polarization and creating informational filter bubbles. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help shape responsible AI? JOIN US
- Alleged deployer
- Alleged developer
- Alleged harmed parties
- political-campaigns, facebook-users
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/522
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.