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インシデント 103: Twitter’s Image Cropping Tool Allegedly Showed Gender and Racial Bias

概要: Twitter's photo cropping algorithm was revealed by researchers to favor white and women faces in photos containing multiple faces, prompting the company to stop its use on mobile platform.

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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: Twitterが開発し提供したAIシステムで、Twitter Users , Twitter non-white users と Twitter non-male users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
103
レポート数
5
インシデント発生日
2020-09-18
エディタ
Sean McGregor, Khoa Lam
Applied Taxonomies
GMF, CSETv1, 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.
 

103

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.
 

yes

Notes (AI special interest intangible harm)

If for 5.5 you select unclear or leave it blank, please provide a brief description of why. You can also add notes if you want to provide justification for a level.
 

The cropping neutral network would crop the preview image in way that focused more on individuals with lighter completions, younger, female, or without disabilities.

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

GMF 分類法のクラス

分類法の詳細

Known AI Goal Snippets

One or more snippets that justify the classification.
 

(Snippet Text: Twitter‘s algorithm for automatically cropping images attached to tweets often doesn’t focus on the important content in them. , Related Classifications: Image Cropping)

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
 

1.1. Unfair discrimination and misrepresentation

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. Discrimination and Toxicity

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

+1
Why Twitter’s image cropping algorithm appears to have white bias
+1
Twitter's Photo Crop Algorithm Favors White Faces and Women
Twitter launches bug bounty contest to detect algorithmic biasTwitter's AI bounty program reveals bias toward young, pretty white people
Why Twitter’s image cropping algorithm appears to have white bias

Why Twitter’s image cropping algorithm appears to have white bias

thenextweb.com

Twitter's Photo Crop Algorithm Favors White Faces and Women

Twitter's Photo Crop Algorithm Favors White Faces and Women

wired.com

Sharing learnings about our image cropping algorithm

Sharing learnings about our image cropping algorithm

blog.twitter.com

Twitter launches bug bounty contest to detect algorithmic bias

Twitter launches bug bounty contest to detect algorithmic bias

engadget.com

Twitter's AI bounty program reveals bias toward young, pretty white people

Twitter's AI bounty program reveals bias toward young, pretty white people

engadget.com

Why Twitter’s image cropping algorithm appears to have white bias
thenextweb.com · 2020

Twitter‘s algorithm for automatically cropping images attached to tweets often doesn’t focus on the important content in them. A bother, for sure, but it seems like a minor one on the surface. However, over the weekend, researchers found th…

Twitter's Photo Crop Algorithm Favors White Faces and Women
wired.com · 2021

A study of 10,000 images found bias in what the system chooses to highlight. Twitter has stopped using it on mobile, and will consider ditching it on the web.

LAST FALL, CANADIAN student Colin Madland noticed that Twitter’s automatic croppi…

Sharing learnings about our image cropping algorithm
blog.twitter.com · 2021

In October 2020, we heard feedback from people on Twitter that our image cropping algorithm didn’t serve all people equitably. As part of our commitment to address this issue, we also shared that we'd analyze our model again for bias. Over …

Twitter launches bug bounty contest to detect algorithmic bias
engadget.com · 2021

Twitter has laid out plans for a bug bounty competition with a difference. This time around, instead of paying researchers who uncover security issues, Twitter will reward those who find as-yet undiscovered examples of bias in its image-cro…

Twitter's AI bounty program reveals bias toward young, pretty white people
engadget.com · 2021

Twitter's first bounty program for AI bias has wrapped up, and there are already some glaring issues the company wants to address. CNET reports that grad student Bogdan Kulynych has discovered that photo beauty filters skew the Twitter sali…

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

よく似たインシデント

テキスト類似度による

Did our AI mess up? Flag the unrelated incidents

Images of Black People Labeled as Gorillas

When It Comes to Gorillas, Google Photos Remains Blind

Jun 2015 · 24 レポート
FaceApp Racial Filters

FaceApp deletes new black, white and Asian filters after racism storm

Apr 2017 · 23 レポート
TayBot

Danger, danger! 10 alarming examples of AI gone wild

Mar 2016 · 28 レポート
前のインシデント次のインシデント

よく似たインシデント

テキスト類似度による

Did our AI mess up? Flag the unrelated incidents

Images of Black People Labeled as Gorillas

When It Comes to Gorillas, Google Photos Remains Blind

Jun 2015 · 24 レポート
FaceApp Racial Filters

FaceApp deletes new black, white and Asian filters after racism storm

Apr 2017 · 23 レポート
TayBot

Danger, danger! 10 alarming examples of AI gone wild

Mar 2016 · 28 レポート

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