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インシデント 18: Gender Biases of Google Image Search

概要: Google Image returns results that under-represent women in leadership roles, notably with the first photo of a female "CEO" being a Barbie doll after 11 rows of male CEOs.

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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: Googleが開発し提供したAIシステムで、Women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
18
レポート数
11
インシデント発生日
2015-04-04
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.
 

18

Notes (special interest intangible harm)

Input any notes that may help explain your answers.
 

Significant gender/sex bias in google search image results

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

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
 

2015

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
 

04

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
 

09

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

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.
 

Yes

Data Inputs

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

open source internet, user requests, user searches

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

+9
Who’s a CEO? Google image results can shift gender biases
Google’s algorithm shows prestigious job ads to men, but not to women. Here’s why that should worry you.Why is it still so hard to find women CEOs on Google Images?
Who’s a CEO? Google image results can shift gender biases

Who’s a CEO? Google image results can shift gender biases

washington.edu

Google Search thinks the most important female CEO is Barbie

Google Search thinks the most important female CEO is Barbie

theverge.com

The first woman CEO to appear in a Google Images search is ... CEO Barbie

The first woman CEO to appear in a Google Images search is ... CEO Barbie

pcworld.com

Be Careful What You Google

Be Careful What You Google

theatlantic.com

Looking for 'doctor' or 'cop' in Google Image Search delivers crazy sexist results

Looking for 'doctor' or 'cop' in Google Image Search delivers crazy sexist results

splinternews.com

Google Image Search Has A Gender Bias Problem

Google Image Search Has A Gender Bias Problem

huffingtonpost.com

When You Google Image CEO, the First Female Photo on the Results Page Is Barbie

When You Google Image CEO, the First Female Photo on the Results Page Is Barbie

glamour.com

Google Image search for CEO has Barbie as first female result

Google Image search for CEO has Barbie as first female result

bbc.co.uk

The Hidden Gender Bias In Google Image Search

The Hidden Gender Bias In Google Image Search

fastcompany.com

Google’s algorithm shows prestigious job ads to men, but not to women. Here’s why that should worry you.

Google’s algorithm shows prestigious job ads to men, but not to women. Here’s why that should worry you.

washingtonpost.com

Why is it still so hard to find women CEOs on Google Images?

Why is it still so hard to find women CEOs on Google Images?

fastcompany.com

Who’s a CEO? Google image results can shift gender biases
washington.edu · 2015

Who’s a CEO? Google image results can shift gender biases

Jennifer Langston UW News

Getty Images last year created a new online image catalog of women in the workplace – one that countered visual stereotypes on the Internet of moms as frazz…

Google Search thinks the most important female CEO is Barbie
theverge.com · 2015

The University of Washington just released a preview of a study that claims search engine results can influence people's perceptions about how many men or women hold certain jobs. One figure quoted in the preview is that in a Google image s…

The first woman CEO to appear in a Google Images search is ... CEO Barbie
pcworld.com · 2015

The Ellen Pao-Kleiner Perkins trial shone a light on discrimination in the tech industry, but for a more immediate look at the challenges women face in corporate America, look no further than a Google Images search.

Doing a search at the si…

Be Careful What You Google
theatlantic.com · 2015

Google is a modern oracle, and a miraculous one at that. It can lead you to the Perfect Strangers theme song lyrics, or to a satellite image of your childhood neighborhood, or to a blueprint for building a quantum computer. But for as much …

Looking for 'doctor' or 'cop' in Google Image Search delivers crazy sexist results
splinternews.com · 2015

In today's modern professional world men can be doctors, investment bankers, and professors, while women, of course, can be nurses, secretaries, and sexy Halloween costume models—at least according to Google Image Search.

Why did we spend a…

Google Image Search Has A Gender Bias Problem
huffingtonpost.com · 2015

Not all doctors or CEOs are men. Not all nurses are women. But you might think otherwise if you searched for these professions in Google images.

It turns out that there's a noticeable gender bias in the image search results for some jobs, a…

When You Google Image CEO, the First Female Photo on the Results Page Is Barbie
glamour.com · 2015

Try this: Google image "CEO." Notice anything? The first female Google image search result for "CEO" appears TWELVE rows down—and it's Barbie.

A recent study conducted at the University of Washington sought to examine how well female repres…

Google Image search for CEO has Barbie as first female result
bbc.co.uk · 2015

Search the term "CEO" in Google Images and the first picture of woman you get is a picture of Barbie in a suit.

This "gender bias" has become apparent after a paper was published showing that many image searches for specific occupations fav…

The Hidden Gender Bias In Google Image Search
fastcompany.com · 2015

Just when you thought biases were a completely human construct, more evidence suggests that both algorithms and interfaces could be biased, too.

ADVERTISEMENT

The latest example of this is from a study conducted by researchers from Universi…

Google’s algorithm shows prestigious job ads to men, but not to women. Here’s why that should worry you.
washingtonpost.com · 2015

Fresh off the revelation that Google image searches for “CEO” only turn up pictures of white men, there’s new evidence that algorithmic bias is, alas, at it again. In a paper published in April, a team of researchers from Carnegie Mellon Un…

Why is it still so hard to find women CEOs on Google Images?
fastcompany.com · 2018

“You can’t be what you can’t see,” Marie Wilson of the White House Project said back in 2010. According to a new study, Google Images may not be helping to improve the situation.

AdView analyzed employment data to determine the number of wo…

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

Sexist and Racist Google Adsense Advertisements

Discrimination in Online Ad Delivery

Jan 2013 · 27 レポート
AI Beauty Judge Did Not Like Dark Skin

A beauty contest was judged by AI and the robots didn't like dark skin

Sep 2016 · 10 レポート
Biased Google Image Results

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

Mar 2016 · 18 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

Sexist and Racist Google Adsense Advertisements

Discrimination in Online Ad Delivery

Jan 2013 · 27 レポート
AI Beauty Judge Did Not Like Dark Skin

A beauty contest was judged by AI and the robots didn't like dark skin

Sep 2016 · 10 レポート
Biased Google Image Results

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

Mar 2016 · 18 レポート

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