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 404: Sound Intelligence's Aggression Detector Misidentified Innocuous Sounds

Description: Sound Intelligence's "aggression detection" algorithm deployed by schools reportedly contained high rates of false positive, misclassifying laughing, coughing, cheering, and loud discussions.

Tools

New ReportNew ReportNew ResponseNew ResponseDiscoverDiscoverView HistoryView History

Entities

View all entities
Alleged: Sound Intelligence developed an AI system deployed by Rock Hill Schools and Pinecrest Academy Horizon, which harmed students , Rock Hill School students and Pinecrest Academy Horizon students.

Incident Stats

Incident ID
404
Report Count
2
Incident Date
2019-06-25
Editors
Khoa Lam
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
 

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

+2
The Unproven, Invasive Surveillance Technology Schools Are Using to Monitor Students
The Unproven, Invasive Surveillance Technology Schools Are Using to Monitor Students

The Unproven, Invasive Surveillance Technology Schools Are Using to Monitor Students

features.propublica.org

Methodology: How We Tested an Aggression Detection Algorithm

Methodology: How We Tested an Aggression Detection Algorithm

projects.propublica.org

The Unproven, Invasive Surveillance Technology Schools Are Using to Monitor Students
features.propublica.org · 2019

Ariella Russcol specializes in drama at the Frank Sinatra School of the Arts in Queens, New York, and the senior's performance on this April afternoon didn't disappoint. While the library is normally the quietest room in the school, her ear…

Methodology: How We Tested an Aggression Detection Algorithm
projects.propublica.org · 2019

Introduction

This companion article to our main story describes the testing and data analysis ProPublica conducted for the Sound Intelligence aggression detection algorithm on the Louroe Digifact A microphone. Here, we discuss the data and …

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

Security Robot Rolls Over Child in Mall

Security Robot Rolls Over Child in Mall

Jul 2016 · 27 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
Amazon Alexa Responding to Environmental Inputs

Amazon Alexa Responding to Environmental Inputs

Dec 2015 · 35 reports
Previous IncidentNext Incident

Similar Incidents

By textual similarity

Did our AI mess up? Flag the unrelated incidents

Security Robot Rolls Over Child in Mall

Security Robot Rolls Over Child in Mall

Jul 2016 · 27 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
Amazon Alexa Responding to Environmental Inputs

Amazon Alexa Responding to Environmental Inputs

Dec 2015 · 35 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

2024 - AI Incident Database

  • Terms of use
  • Privacy Policy
  • Open twitterOpen githubOpen rssOpen facebookOpen linkedin
  • 300d90c