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 203: Uber Launched Opaque Algorithm That Changes Drivers' Payments in the US

Description: Uber launched a new but opaque algorithm to determine drivers' pay in the US which allegedly caused drivers to experience lower fares, confusing fare drops, and a decrease in rides.

Tools

New ReportNew ReportNew ResponseNew ResponseDiscoverDiscoverView HistoryView History

Entities

View all entities
Alleged: Uber developed and deployed an AI system, which harmed Uber drivers.

Incident Stats

Incident ID
203
Report Count
3
Incident Date
2022-02-10
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
 

7.4. Lack of transparency or interpretability

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
 

Intentional

Incident Reports

Reports Timeline

Incident OccurrenceUber revamps driver pay algorithm in large U.S. pilot to attract driversSecretive Algorithm Will Now Determine Uber Driver Pay in Many Cities – The MarkupUber tests new algorithm in the US that can change driver payments
Uber revamps driver pay algorithm in large U.S. pilot to attract drivers

Uber revamps driver pay algorithm in large U.S. pilot to attract drivers

reuters.com

Secretive Algorithm Will Now Determine Uber Driver Pay in Many Cities – The Markup

Secretive Algorithm Will Now Determine Uber Driver Pay in Many Cities – The Markup

themarkup.org

Uber tests new algorithm in the US that can change driver payments

Uber tests new algorithm in the US that can change driver payments

labsnews.com

Uber revamps driver pay algorithm in large U.S. pilot to attract drivers
reuters.com · 2022

Feb 25 (Reuters) - Uber Technologies Inc (UBER.N) is testing a new driver earnings algorithm in 24 U.S. cities that allows drivers to see pay and destinations before accepting a trip and raises the incentives for drivers to take short rides…

Secretive Algorithm Will Now Determine Uber Driver Pay in Many Cities – The Markup
themarkup.org · 2022

The company has long used ride time and mileage to decide driver pay but is now turning to an opaque calculation called “Upfront Fares”

Uber has quietly changed the way it pays drivers in several major cities across the U.S., using a new fe…

Uber tests new algorithm in the US that can change driver payments
labsnews.com · 2022

Uber is testing a new driver earnings algorithm in 24 U.S. cities that allows drivers to see pay and destinations before accepting a trip and raises the incentives for drivers to take short rides in an effort to attract more drivers. In Bra…

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.
Previous IncidentNext Incident

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
  • 30ebe76