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インシデント 87: UK passport photo checker shows bias against dark-skinned women

概要: UK passport photo checker shows bias against dark-skinned women.

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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: UK Home Officeが開発し提供したAIシステムで、dark-skinned people と dark-skinned 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
87
レポート数
1
インシデント発生日
2020-10-07
エディタ
Sean McGregor, Khoa Lam
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

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.
 

Yes

Data Inputs

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

Passport IDs

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.
 

87

Notes (special interest intangible harm)

Input any notes that may help explain your answers.
 

The report focused on differential treatment to black women. However, it also showed differential treatment based individually on gender and lightness of skin tone.

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
 

2020

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
 

1.3. Unequal performance across groups

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
UK passport photo checker shows bias against dark-skinned women
UK passport photo checker shows bias against dark-skinned women

UK passport photo checker shows bias against dark-skinned women

bbc.co.uk

UK passport photo checker shows bias against dark-skinned women
bbc.co.uk · 2020

Women with darker skin are more than twice as likely to be told their photos fail UK passport rules when they submit them online than lighter-skinned men, according to a BBC investigation.

One black student said she was wrongly told her mou…

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

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

ETS Used Allegedly Flawed Voice Recognition Evidence to Accuse and Assess Scale of Cheating, Causing Thousands to be Deported from the UK

The English test that ruined thousands of lives

Jan 2014 · 1 レポート
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Robot passport checker rejects Asian man's photo for having his eyes closed

Dec 2016 · 22 レポート
Opaque Fraud Detection Algorithm by the UK’s Department of Work and Pensions Allegedly Discriminated against People with Disabilities

DWP urged to reveal algorithm that ‘targets’ disabled for benefit fraud

Oct 2019 · 6 レポート

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