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Incident 364: Walmart's Bagging-Detection False Positives Exposed Workers to Health Risk

Description: Walmart's theft-deterring bagging-detection system allegedly exposed workers to health risks during the coronavirus pandemic when its false positives prompted workers to unnecessarily step in to resolve the issue.

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Alleged: Everseen developed an AI system deployed by Walmart, which harmed Walmart employees.

Incident Stats

Incident ID
364
Report Count
1
Incident Date
2020-04-15
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

Incident OccurrenceSelf-Checkout Headaches May Be Putting Walmart Workers Too Close To Shoppers
Self-Checkout Headaches May Be Putting Walmart Workers Too Close To Shoppers

Self-Checkout Headaches May Be Putting Walmart Workers Too Close To Shoppers

huffpost.com

Self-Checkout Headaches May Be Putting Walmart Workers Too Close To Shoppers
huffpost.com · 2020

When Walmart began rolling out a new theft-deterrence system three years ago, the retail giant hoped it would improve the self-checkout experience for shoppers while saving the company money.

The artificial-intelligence-based system, develo…

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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