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インシデント 47: LinkedIn Search Prefers Male Names

概要: An investigation by The Seattle Times in 2016 found a gender bias in LinkedIn's search engine.

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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: LinkedIn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
47
レポート数
9
インシデント発生日
2016-09-06
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.
 

47

Notes (special interest intangible harm)

Input any notes that may help explain your answers.
 

LinkedIn's search suggestion algorithm prompted users searching for female names to choose similar-sounding male names instead.

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
 

2016

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
 

08

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
 

31

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.
 

Medium

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.
 

words appearing in user past queries and member profiles

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

+2
LinkedIn's search engine may reflect a gender bias
LinkedIn’s search algorithm apparently favored men until this week+3
LinkedIn Tweaks Search Engine After Gender Bias Allegations
Bye Bye, Gender Bias! LinkedIn Will No Longer Ask Users If They Meant to Search for a ManDoes LinkedIn have a gender bias?
LinkedIn's search engine may reflect a gender bias

LinkedIn's search engine may reflect a gender bias

stuff.co.nz

Tech companies work toward website searches free of bias

Tech companies work toward website searches free of bias

startribune.com

LinkedIn’s search algorithm apparently favored men until this week

LinkedIn’s search algorithm apparently favored men until this week

qz.com

LinkedIn Tweaks Search Engine After Gender Bias Allegations

LinkedIn Tweaks Search Engine After Gender Bias Allegations

time.com

LinkedIn investigation claims searches for female professionals end up suggesting MEN

LinkedIn investigation claims searches for female professionals end up suggesting MEN

dailymail.co.uk

LinkedIn denies gender bias claim over site search

LinkedIn denies gender bias claim over site search

bbc.com

LinkedIn Denies Gender-Bias Problem

LinkedIn Denies Gender-Bias Problem

thecut.com

Bye Bye, Gender Bias! LinkedIn Will No Longer Ask Users If They Meant to Search for a Man

Bye Bye, Gender Bias! LinkedIn Will No Longer Ask Users If They Meant to Search for a Man

glamour.com

Does LinkedIn have a gender bias?

Does LinkedIn have a gender bias?

digitaljournal.com

LinkedIn's search engine may reflect a gender bias
stuff.co.nz · 2016

LinkedIn says its suggested results are generated automatically by an analysis of the tendencies of past searchers.

Search for a female contact on LinkedIn, and you may get a curious result. The professional networking website asks if you m…

Tech companies work toward website searches free of bias
startribune.com · 2016

– Search for a female ­contact on LinkedIn, and you might get a curious result. The professional networking website asks if you meant to search for a similar-looking man's name.

A search for "Stephanie Williams," for example, brings up a pr…

LinkedIn’s search algorithm apparently favored men until this week
qz.com · 2016

Until Sep. 7, LinkedIn users searching for female contacts on the site may have noticed some strange results. Searches for common female names were yielding suggestions for male names as well.

Take a LinkedIn search for “Stephanie Williams.…

LinkedIn Tweaks Search Engine After Gender Bias Allegations
time.com · 2016

MOTTO Samantha Cooney is the content strategy editor at TIME.

A week after the Seattle Times reported that LinkedIn’s search engine may reflect a gender bias, the networking platform announced a tweak to its search algorithm.

The Times repo…

LinkedIn investigation claims searches for female professionals end up suggesting MEN
dailymail.co.uk · 2016

The question of whether a computer can be biased or not may seem frivolous, but it could make all the difference when it comes to being found online.

Now, an investigation by a US newspaper has suggested that this bias may be present on the…

LinkedIn denies gender bias claim over site search
bbc.com · 2016

Image copyright Getty Images Image caption LinkedIn launched in 2002

LinkedIn has denied that its search algorithm has been biased towards suggesting male versions of female names in searches on its website.

A Seattle Times investigation fo…

LinkedIn Denies Gender-Bias Problem
thecut.com · 2016

LinkedIn.

Last week, a Seattle Times investigation revealed that LinkedIn’s search function seems to have a pretty pronounced gender bias. It turns out, when you search for a woman’s name on LinkedIn, the site has a pesky habit of asking wh…

Bye Bye, Gender Bias! LinkedIn Will No Longer Ask Users If They Meant to Search for a Man
glamour.com · 2016

Have you ever searched for a contact on LinkedIn only to have the networking site automatically prompt you to look up a man with a similar name? You're not alone. A recent investigation by the Seattle Times revealed a pervasive gender bias …

Does LinkedIn have a gender bias?
digitaljournal.com · 2016

Sep 19, 2016 in Technology The ‘professional’ social networking site LinkedIn has been accused of having a gender bias. This is through providing more male professionals in its search results than females. "A Fresh Conversation on Gender is…

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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