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インシデント 56: AI-Designed Phone Cases Are Unexpected

概要: A third-party Amazon merchant named “my_handy_design” was suspected of using a bot to generate cell phone case designs based on the bizarre and unattractive designs being offered.

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新しいレポート新しいレポート新しいレスポンス新しいレスポンス発見する発見する履歴を表示履歴を表示

組織

すべての組織を表示
推定: my_handy_designが開発し提供したAIシステムで、my_handy_designに影響を与えた

インシデントのステータス

インシデントID
56
レポート数
7
インシデント発生日
2017-07-10
エディタ
Sean McGregor
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.
 

56

AI Tangible Harm Level Notes

Notes about the AI tangible harm level assessment
 

AI system or bot used to create phone cases.

Notes (special interest intangible harm)

Input any notes that may help explain your answers.
 

Although the phone cases may be strange and unusual, there was no 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
 

2017

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
 

07

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.
 

Specification, Assurance

Physical System

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

Unknown/unclear

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.
 

Unclear/unknown

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.
 

Unknown

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

+3
Amazon AI Designed To Create Phone Cases Goes Hilariously Wrong
+2
Amazon A.I. Making Cell Phone Cases Gets It Horribly Wrong (NSFW)
Amazon Seller Uses AI To Create Phone Cases, But Things Go Hilariously Out Of Hand
Amazon AI Designed To Create Phone Cases Goes Hilariously Wrong

Amazon AI Designed To Create Phone Cases Goes Hilariously Wrong

iflscience.com

An Amazon Bot Is Making The Greatest Smartphone Cases

An Amazon Bot Is Making The Greatest Smartphone Cases

gizmodo.com.au

A bot on Amazon is making the best, worst smartphones cases

A bot on Amazon is making the best, worst smartphones cases

theverge.com

Amazon A.I. Making Cell Phone Cases Gets It Horribly Wrong (NSFW)

Amazon A.I. Making Cell Phone Cases Gets It Horribly Wrong (NSFW)

offthemainpage.com

The automated Amazon seller making the worst phone cases ever

The automated Amazon seller making the worst phone cases ever

theguardian.com

This Amazon bot is making the best phone cases we've ever seen

This Amazon bot is making the best phone cases we've ever seen

mashable.com

Amazon Seller Uses AI To Create Phone Cases, But Things Go Hilariously Out Of Hand

Amazon Seller Uses AI To Create Phone Cases, But Things Go Hilariously Out Of Hand

justsomething.co

Amazon AI Designed To Create Phone Cases Goes Hilariously Wrong
iflscience.com · 2017

An Amazon AI bot that was created to design smartphone cases has gone delightfully and hilariously wrong. The Amazon bot is supposed to generate phone cases based on popular image searches, but unfortunately the bot seems to have gone awry …

An Amazon Bot Is Making The Greatest Smartphone Cases
gizmodo.com.au · 2017

In what's either the best art project, the best business move, or both, someone has made what I presume is a bot that's churning out thousands of unique iPhone cases for sale on Amazon. Pretty much all of them are masterpieces.

Bots mocking…

A bot on Amazon is making the best, worst smartphones cases
theverge.com · 2017

What do a collection of wooden spoons, a newly installed rain gutter, and a dialysis machine have in common? They’d all look fantastic on your next smartphone case according to my-handy-design — a bot on Amazon that’s currently going hog wi…

Amazon A.I. Making Cell Phone Cases Gets It Horribly Wrong (NSFW)
offthemainpage.com · 2017

1.1k SHARES Share Tweet Reddit Pinterest Stumbleupon Linkedin

An A.I. bot created by Amazon to specifically design smartphone cases has outdone itself in ways you cannot even imagine. This bot is supposed to be generating cell phone cases b…

The automated Amazon seller making the worst phone cases ever
theguardian.com · 2017

Amazon truly is the website where you can buy anything and everything: books, games, wine – and a Samsung Galaxy S5 case decorated with a pixelated image of a “doctor adjustable angle knee brace support for leg or knee injur”. Yes, missing …

This Amazon bot is making the best phone cases we've ever seen
mashable.com · 2017

Amazon Prime Day is nearly here, but there's something else catching people's attention on Amazon this week.

A seller that appears to be a bot is making cases for iPhones and Samsung phones with bizarre stock images — and they're quite poss…

Amazon Seller Uses AI To Create Phone Cases, But Things Go Hilariously Out Of Hand
justsomething.co · 2017

Share this post on FB:

Amazon seller My Handy Design, uses artificial intelligence to create designs for their phone cases, however, as you scroll through its product gallery, it’s evident that things have seriously gone out of hand. The bo…

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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FaceApp Racial Filters

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