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インシデント 80: AI mistakes referee’s bald head for football — hilarity ensued

概要: In a Scottish soccer match the AI-enabled ball-tracking camera used to livestream the game repeatedly tracked an official’s bald head as though it were the soccer ball.

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組織

すべての組織を表示
Alleged: unknown developed an AI system deployed by Inverness Caledonian Thistle Football Club, which harmed livestream viewers.

インシデントのステータス

インシデントID
80
レポート数
2
インシデント発生日
2020-10-24
エディタ
Sean McGregor, Khoa Lam
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

CSETv1 分類法のクラス

分類法の詳細

Incident Number

The number of the incident in the AI Incident Database.
 

80

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
 

10

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
 

24

Estimated Date

“Yes” if the data was estimated. “No” otherwise.
 

No

CSETv0 分類法のクラス

分類法の詳細

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.
 

Robustness

Physical System

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

Consumer device

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.
 

video feed, pre-tagged soccer match imagery

MIT 分類法のクラス

Machine-Classified
分類法の詳細

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

インシデントレポート

レポートタイムライン

Incident OccurrenceSoccer match ruined when AI-controlled camera mistakes ref’s bald head for ballAI mistakes referee’s bald head for football — hilarity ensued
Soccer match ruined when AI-controlled camera mistakes ref’s bald head for ball

Soccer match ruined when AI-controlled camera mistakes ref’s bald head for ball

sbnation.com

AI mistakes referee’s bald head for football — hilarity ensued

AI mistakes referee’s bald head for football — hilarity ensued

thenextweb.com

Soccer match ruined when AI-controlled camera mistakes ref’s bald head for ball
sbnation.com · 2020

Technology in sports is a beautiful thing, but sometimes even the greatest inventions can go wrong. This happened over the weekend in a soccer game in Scotland, when an AI-controlled camera got confused, and thought a lineman’s bald head wa…

AI mistakes referee’s bald head for football — hilarity ensued
thenextweb.com · 2020

Top football leagues and teams around the world have TV crews and streaming services at their disposal to broadcast matches to fans across the globe. However, because of the coronavirus pandemic, smaller football teams are relying on AI-pow…

バリアント

「バリアント」は既存のAIインシデントと同じ原因要素を共有し、同様な被害を引き起こし、同じ知的システムを含んだインシデントです。バリアントは完全に独立したインシデントとしてインデックスするのではなく、データベースに最初に投稿された同様なインシデントの元にインシデントのバリエーションとして一覧します。インシデントデータベースの他の投稿タイプとは違い、バリアントではインシデントデータベース以外の根拠のレポートは要求されません。詳細についてはこの研究論文を参照してください

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前のインシデント次のインシデント

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テキスト類似度による

Did our AI mess up? Flag the unrelated incidents

Security Robot Drowns Itself in a Fountain

DC security robot quits job by drowning itself in a fountain

Jul 2017 · 30 レポート
Biased Google Image Results

'Black teenagers' vs. 'white teenagers': Why Google's algorithm displays racist results

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Game AI System Produces Imbalanced Game

6 goof-ups that show AI is still in its diapers

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