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Incident 134: Robot in Chinese Shopping Mall Fell off the Escalator, Knocking down Passengers

Description: A shopping guide robot deployed by the Fuzhou Zhongfang Marlboro Mall was shown on video allegedly walking to the escalator by itself, falling down, and knocking over passengers, which prompted its suspension.

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Alleged: unknown developed an AI system deployed by Fuzhou Zhongfang Marlboro Mall, which harmed Fuzhou Zhongfang Marlboro Mall goers.

Incident Stats

Incident ID
134
Report Count
2
Incident Date
2020-12-25
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.
 

134

AI Tangible Harm Level Notes

Notes about the AI tangible harm level assessment
 

Unclear if there was AI in the robot.

Special Interest Intangible Harm

An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.
 

no

Date of Incident Year

The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank. Enter in the format of YYYY
 

2020

Date of Incident Month

The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank. Enter in the format of MM
 

12

Date of Incident Day

The day on which the incident occurred. If a precise date is unavailable, leave blank. Enter in the format of DD
 

25

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 Occurrence+1
Shopping mall robot fell off the escalator and knocked down passengers
Shopping mall robot fell off the escalator and knocked down passengers

Shopping mall robot fell off the escalator and knocked down passengers

s.weibo.com

Weibo post: Sino-Singapore Jingwei

Weibo post: Sino-Singapore Jingwei

weibo.com

Shopping mall robot fell off the escalator and knocked down passengers
s.weibo.com · 2020

(Translated from Chinese by Google Translate)

On December 25, a shopping guide robot in Fuzhou Zhongfang Marlboro Mall fell off the escalator and knocked over passengers. On the 27th, it was learned that the robot had been suspended. The pe…

Weibo post: Sino-Singapore Jingwei
weibo.com · 2020
AI Translated

[#Shopping mall robot fell off the escalator and knocked down the passengers#Shopping mall: The robot company is analyzing the dissection] On December 25, the shopping guide robot in the Fuzhou Zhongfang Wanbao City mall fell off the escala…

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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