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 116: Amazon's AI Cameras Incorrectly Penalized Delivery Drivers for Mistakes They Did Not Make

Description: Amazon's automated performance evaluation system involving AI-powered cameras incorrectly punished delivery drivers for non-existent mistakes, impacting their chances for bonuses and rewards.

Tools

New ReportNew ReportNew ResponseNew ResponseDiscoverDiscoverView HistoryView History

Entities

View all entities
Alleged: Netradyne developed an AI system deployed by Amazon, which harmed Amazon delivery drivers and Amazon workers.

Incident Stats

Incident ID
116
Report Count
2
Incident Date
2021-09-20
Editors
Sean McGregor, Khoa Lam
Applied Taxonomies
CSETv1, GMF, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

116

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

+1
Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make
AI Is Penalizing Amazon Delivery Drivers for Errors They Aren't Making
Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make

Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make

vice.com

AI Is Penalizing Amazon Delivery Drivers for Errors They Aren't Making

AI Is Penalizing Amazon Delivery Drivers for Errors They Aren't Making

extremetech.com

Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make
vice.com · 2021

“Maintain safe distance,” the camera installed above his seat would say when a car cut him off. That data would be sent to Amazon, and would be used to evaluate his performance that week and determine whether he got a bonus.

In early 2021, …

AI Is Penalizing Amazon Delivery Drivers for Errors They Aren't Making
extremetech.com · 2021

Concerns about artificial intelligence and its impact on work are not new, but as more companies deploy these solutions we’re seeing decided snags in the process. One point many of these conversations take for granted is that AI-powered too…

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

Amazon Flex Drivers Allegedly Fired via Automated Employee Evaluations

Amazon Flex Drivers Allegedly Fired via Automated Employee Evaluations

Sep 2015 · 5 reports
Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees

Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees

Aug 2014 · 10 reports
YouTuber Tested Tesla on Self Driving Mode, Colliding with Street Pylons

YouTuber Tested Tesla on Self Driving Mode, Colliding with Street Pylons

Feb 2022 · 3 reports
Previous IncidentNext Incident

Similar Incidents

By textual similarity

Did our AI mess up? Flag the unrelated incidents

Amazon Flex Drivers Allegedly Fired via Automated Employee Evaluations

Amazon Flex Drivers Allegedly Fired via Automated Employee Evaluations

Sep 2015 · 5 reports
Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees

Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees

Aug 2014 · 10 reports
YouTuber Tested Tesla on Self Driving Mode, Colliding with Street Pylons

YouTuber Tested Tesla on Self Driving Mode, Colliding with Street Pylons

Feb 2022 · 3 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
  • 8b8f151