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Incident 580: Alleged Gender Discrimination in Facebook Job Ads Algorithm

Description: Facebook's ad delivery algorithm allegedly disproportionately showed job advertisements to one gender. Despite claims of non-discrimination, the algorithm's actions seem to perpetuate societal biases, which in turn could potentially limit opportunities for certain groups and hinder gender equity in the workplace.

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Alleged: Meta developed and deployed an AI system, which harmed Women , Underrepresented genders , General public and Advertisers.

Incident Stats

Incident ID
580
Report Count
1
Incident Date
2023-06-12
Editors
Applied Taxonomies
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
New evidence of Facebook’s sexist algorithm
New evidence of Facebook’s sexist algorithm

New evidence of Facebook’s sexist algorithm

globalwitness.org

New evidence of Facebook’s sexist algorithm
globalwitness.org · 2023

All the ads that we posted were shown - via one of Facebook’s mandatory ad campaign objectives – to users that Facebook thought were most likely to click on the website they linked to. And for all the ads, we specified only the following: t…

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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