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Incident 83: AI Spam Filters Allegedly Block Legitimate Emails Based on Biased Keyword Detection

Description: AlgorithmWatch tested spam filtering algorithms across Gmail, Yahoo, Outlook, GMX, and LaPoste. Their findings reportedly showed that Microsoft Outlook’s spam filter flagged emails based on specific keywords that led to racial and content-based biases blocking legitimate communications. Emails mentioning Nigeria or containing certain financial and sexual health terms were found to be disproportionately marked as spam.

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Entities

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Alleged: Yahoo , LaPoste , GMX , Microsoft and Google developed an AI system deployed by Yahoo , Outlook , LaPoste , GMX and Gmail, which harmed Yahoo! Mail users , Microsoft Outlook users , LaPoste users , GMX users and Gmail Users.
Alleged implicated AI systems: Yahoo! Mail's spam filter , SpamAssassin , Microsoft Outlook's spam filter , LaPoste Mail's spam filter , GMX Mail's spam filter and Gmail's spam filter

Incident Stats

Incident ID
83
Report Count
1
Incident Date
2020-10-22
Editors
Khoa Lam, Sean McGregor, Daniel Atherton
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

CSETv0 Taxonomy Classifications

Taxonomy Details

Problem Nature

Indicates which, if any, of the following types of AI failure describe the incident: "Specification," i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; "Robustness," i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; "Assurance," i.e. the system could not be adequately monitored or controlled during operation.
 

Specification, Robustness

Physical System

Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.
 

Software only

Level of Autonomy

The degree to which the AI system(s) functions independently from human intervention. "High" means there is no human involved in the system action execution; "Medium" means the system generates a decision and a human oversees the resulting action; "low" means the system generates decision-support output and a human makes a decision and executes an action.
 

Unclear/unknown

Nature of End User

"Expert" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; "Amateur" if the AI systems were primarily meant to benefit the general public or untrained users.
 

Amateur

Public Sector Deployment

"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
 

No

Data Inputs

A brief description of the data that the AI system(s) used or were trained on.
 

inbound emails

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

83

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
Spam filters are efficient and uncontroversial. Until you look at them.
Spam filters are efficient and uncontroversial. Until you look at them.

Spam filters are efficient and uncontroversial. Until you look at them.

algorithmwatch.org

Spam filters are efficient and uncontroversial. Until you look at them.
algorithmwatch.org · 2020

An experiment reveals that Microsoft Outlook marks messages as spam on the basis of a single word, such as “Nigeria”. Spam filters are largely unaudited and could discriminate unfairly.

In an experiment, AlgorithmWatch sent a few hundred em…

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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