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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

Incident ID
213
Report Count
5
Incident Date
2020-07-01
Editors
Khoa Lam
Applied Taxonomies
GMF, MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.3. Lack of capability or robustness

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. AI system safety, failures, and limitations

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

Incident Occurrence+4
Summary of findings: An audit of Facebook’s political ad policy enforcement
Summary of findings: An audit of Facebook’s political ad policy enforcement

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

cybersecurityfordemocracy.org

An Audit of Facebook’s Political Ad Policy Enforcement

An Audit of Facebook’s Political Ad Policy Enforcement

osf.io

How political advertisers get away with skirting Facebook’s rules

How political advertisers get away with skirting Facebook’s rules

protocol.com

Facebook misidentified thousands of political ads: Study

Facebook misidentified thousands of political ads: Study

straitstimes.com

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

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

gizmodo.com

Summary of findings: An audit of Facebook’s political ad policy enforcement
cybersecurityfordemocracy.org · 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…

An Audit of Facebook’s Political Ad Policy Enforcement
osf.io · 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…

How political advertisers get away with skirting Facebook’s rules
protocol.com · 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 misidentified thousands of political ads: Study
straitstimes.com · 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.

Researchers…

Facebook's Political Ad Promises Mostly Miss the Mark, Study Shows
gizmodo.com · 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…

Variants

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.
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