Skip to Content
logologo
AI Incident Database
Open TwitterOpen RSS FeedOpen FacebookOpen LinkedInOpen GitHub
Open Menu
Discover
Submit
  • Welcome to the AIID
  • Discover Incidents
  • Spatial View
  • Table View
  • List view
  • Entities
  • Taxonomies
  • Submit Incident Reports
  • Submission Leaderboard
  • Blog
  • AI News Digest
  • Risk Checklists
  • Random Incident
  • Sign Up
Collapse
Discover
Submit
  • Welcome to the AIID
  • Discover Incidents
  • Spatial View
  • Table View
  • List view
  • Entities
  • Taxonomies
  • Submit Incident Reports
  • Submission Leaderboard
  • Blog
  • AI News Digest
  • Risk Checklists
  • Random Incident
  • Sign Up
Collapse

Incident 161: Facebook's Ad Delivery Reportedly Excluded Audience along Racial and Gender Lines

Description: Facebook's housing and employment ad delivery process allegedly resulted in skews in exposure for some users along demographic lines such as gender and racial identity.

Tools

New ReportNew ReportNew ResponseNew ResponseDiscoverDiscoverView HistoryView History

Entities

View all entities
Alleged: Facebook developed and deployed an AI system, which harmed female Facebook users , Black Facebook users and male Facebook users.

Incident Stats

Incident ID
161
Report Count
3
Incident Date
2019-04-03
Editors
Sean McGregor, 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
 

1.1. Unfair discrimination and misrepresentation

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. Discrimination and Toxicity

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

+1
Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes
+1
Facebook’s ad algorithms are still excluding women from seeing jobs
Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes

Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes

arxiv.org

Facebook’s ad algorithms are still excluding women from seeing jobs

Facebook’s ad algorithms are still excluding women from seeing jobs

technologyreview.com

Auditing for Discrimination in Algorithms Delivering Job Ads

Auditing for Discrimination in Algorithms Delivering Job Ads

arxiv.org

Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes
arxiv.org · 2019

The enormous financial success of online advertising platforms is partially due to the precise targeting features they offer. Although researchers and journalists have found many ways that advertisers can target---or exclude---particular gr…

Facebook’s ad algorithms are still excluding women from seeing jobs
technologyreview.com · 2021

Facebook is withholding certain job ads from women because of their gender, according to the latest audit of its ad service.

The audit, conducted by independent researchers at the University of Southern California (USC), reveals that Facebo…

Auditing for Discrimination in Algorithms Delivering Job Ads
arxiv.org · 2021

Ad platforms such as Facebook, Google and LinkedIn promise value for advertisers through their targeted advertising. However, multiple studies have shown that ad delivery on such platforms can be skewed by gender or race due to hidden algor…

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.

Similar Incidents

By textual similarity

Did our AI mess up? Flag the unrelated incidents

High-Toxicity Assessed on Text Involving Women and Minority Groups

High-Toxicity Assessed on Text Involving Women and Minority Groups

Feb 2017 · 9 reports
COMPAS Algorithm Performs Poorly in Crime Recidivism Prediction

COMPAS Algorithm Performs Poorly in Crime Recidivism Prediction

May 2016 · 22 reports
Gender Biases in Google Translate

Gender Biases in Google Translate

Apr 2017 · 10 reports
Previous IncidentNext Incident

Similar Incidents

By textual similarity

Did our AI mess up? Flag the unrelated incidents

High-Toxicity Assessed on Text Involving Women and Minority Groups

High-Toxicity Assessed on Text Involving Women and Minority Groups

Feb 2017 · 9 reports
COMPAS Algorithm Performs Poorly in Crime Recidivism Prediction

COMPAS Algorithm Performs Poorly in Crime Recidivism Prediction

May 2016 · 22 reports
Gender Biases in Google Translate

Gender Biases in Google Translate

Apr 2017 · 10 reports

Research

  • Defining an “AI Incident”
  • Defining an “AI Incident Response”
  • Database Roadmap
  • Related Work
  • Download Complete Database

Project and Community

  • About
  • Contact and Follow
  • Apps and Summaries
  • Editor’s Guide

Incidents

  • All Incidents in List Form
  • Flagged Incidents
  • Submission Queue
  • Classifications View
  • Taxonomies

2023 - AI Incident Database

  • Terms of use
  • Privacy Policy
  • Open twitterOpen githubOpen rssOpen facebookOpen linkedin
  • 5fc5e5b