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Incident 7: Wikipedia Vandalism Prevention Bot Loop

Description: Wikipedia bots meant to remove vandalism clash with each other and form feedback loops of repetitve undoing of the other bot's edits.

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Entities

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Alleged: Wikipedia developed and deployed an AI system, which harmed Wikimedia Foundation , Wikipedia Editors and Wikipedia Users.

Incident Stats

Incident ID
7
Report Count
6
Incident Date
2017-02-24
Editors
Sean McGregor
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

7

AI Tangible Harm Level Notes

Notes about the AI tangible harm level assessment
 

It is unclear if any of the Wikipedia bots under study relies on machine learning technology, but it is unlikely. Nobody experienced any harm.

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
 

2001

CSETv0 Taxonomy Classifications

Taxonomy Details

Problem Nature

Indicates which, if any, of the following types of AI failure describe the incident: "Specification," i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; "Robustness," i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; "Assurance," i.e. the system could not be adequately monitored or controlled during operation.
 

Specification, Robustness, Assurance

Physical System

Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.
 

Software only

Level of Autonomy

The degree to which the AI system(s) functions independently from human intervention. "High" means there is no human involved in the system action execution; "Medium" means the system generates a decision and a human oversees the resulting action; "low" means the system generates decision-support output and a human makes a decision and executes an action.
 

High

Nature of End User

"Expert" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; "Amateur" if the AI systems were primarily meant to benefit the general public or untrained users.
 

Amateur

Public Sector Deployment

"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
 

No

Data Inputs

A brief description of the data that the AI system(s) used or were trained on.
 

Wikipedia articles, edits from other bots

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
Study reveals bot-on-bot editing wars raging on Wikipedia's pages
Automated Wikipedia Edit-Bots Have Been Fighting Each Other For A Decade+1
Internet Bots Fight Each Other Because They're All Too Human
Danger, danger! 10 alarming examples of AI gone wild
Study reveals bot-on-bot editing wars raging on Wikipedia's pages

Study reveals bot-on-bot editing wars raging on Wikipedia's pages

theguardian.com

People built AI bots to improve Wikipedia. Then they started squabbling in petty edit wars, sigh

People built AI bots to improve Wikipedia. Then they started squabbling in petty edit wars, sigh

theregister.co.uk

Automated Wikipedia Edit-Bots Have Been Fighting Each Other For A Decade

Automated Wikipedia Edit-Bots Have Been Fighting Each Other For A Decade

huffingtonpost.com.au

Internet Bots Fight Each Other Because They're All Too Human

Internet Bots Fight Each Other Because They're All Too Human

wired.com

Wiki Bots That Feud for Years Highlight the Troubled Future of AI

Wiki Bots That Feud for Years Highlight the Troubled Future of AI

seeker.com

Danger, danger! 10 alarming examples of AI gone wild

Danger, danger! 10 alarming examples of AI gone wild

infoworld.com

Study reveals bot-on-bot editing wars raging on Wikipedia's pages
theguardian.com · 2017

For many it is no more than the first port of call when a niggling question raises its head. Found on its pages are answers to mysteries from the fate of male anglerfish, the joys of dorodango, and the improbable death of Aeschylus.

But ben…

People built AI bots to improve Wikipedia. Then they started squabbling in petty edit wars, sigh
theregister.co.uk · 2017

Analysis An investigation into Wikipedia bots has confirmed the automated editing software can be just as pedantic and petty as humans are – often engaging in online spats that can continue for years.

What's interesting is that bots behave …

Automated Wikipedia Edit-Bots Have Been Fighting Each Other For A Decade
huffingtonpost.com.au · 2017

It turns out Wikipedia's automated edit 'bots' have been waging a cyber-war between each other for over a decade by changing each other's corrections -- and it's getting worse.

Researchers at the University of Oxford in the United Kingdom r…

Internet Bots Fight Each Other Because They're All Too Human
wired.com · 2017

Getty Images

No one saw the crisis coming: a coordinated vandalistic effort to insert Squidward references into articles totally unrelated to Squidward. In 2006, Wikipedia was really starting to get going, and really couldn’t afford to have…

Wiki Bots That Feud for Years Highlight the Troubled Future of AI
seeker.com · 2017

Wiki Bots That Feud for Years Highlight the Troubled Future of AI

The behavior of bots is often unpredictable and sometimes leads them to produce errors over and over again in a potentially infinite feedback loop.

Danger, danger! 10 alarming examples of AI gone wild
infoworld.com · 2017

Science fiction is lousy with tales of artificial intelligence run amok. There's HAL 9000, of course, and the nefarious Skynet system from the "Terminator" films. Last year, the sinister AI Ultron came this close to defeating the Avengers, …

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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