Incident 213: Facebook’s Political Ad Detection Reportedly Showed High and Geographically Uneven Error Rates

Description: The performance of Facebook’s political ad detection was revealed by researchers to be imprecise, uneven across countries in errors, and inadequate for preventing systematic violations of political advertising policies.


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Alleged: Facebook developed and deployed an AI system, which harmed Facebook users.

Incident Stats

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Khoa Lam

Incident Reports

An Audit of Facebook’s Political Ad Policy Enforcement · 2021

Major technology companies strive to protect the integrity of political advertising on their platforms by implementing and enforcing self-regulatory policies that impose transparency requirements on political ads. In this paper, we quantify…

Summary of findings: An audit of Facebook’s political ad policy enforcement · 2021

In the first known study to quantify the performance of Facebook’s political ad policy enforcement at a large and representative scale, researchers found that when making decisions on how to classify undeclared ads, Facebook often missed po…

How political advertisers get away with skirting Facebook’s rules · 2021

A new study shows that the vast majority of the time Facebook has made an enforcement decision on a political ad after it ran, it’s made the wrong call.

Political advertisers on Facebook are supposed to identify themselves as such. That way…

Facebook's Political Ad Promises Mostly Miss the Mark, Study Shows · 2021

Researchers found thousands of cases where advertisers skirted the company's rules without ever being flagged.

In the years since the Cambridge Analytica scandal revealed how easily Facebook’s political ads could be weaponized by bad actors…

Facebook misidentified thousands of political ads: Study · 2021

PARIS (AFP) - Facebook misidentified tens of thousands of advertisements flagged under its political ads policy, according to a study released Thursday (Dec 9), which warned that the failure could lead to political manipulation.



A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.