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インシデント 74: Detroit Police Wrongfully Arrested Black Man Due To Faulty FRT

レスポンスしました
概要: A Black man was wrongfully detained by the Detroit Police Department as a result of a false facial recognition (FRT) result.

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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
Alleged: DataWorks Plus developed an AI system deployed by Detroit Police Department, which harmed Robert Julian-Borchak Williams と Black people in Detroit.

インシデントのステータス

インシデントID
74
レポート数
11
インシデント発生日
2020-01-30
エディタ
Sean McGregor
Applied Taxonomies
CSETv0, 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.
 

74

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: On a Thursday afternoon in January, Robert Julian-Borchak Williams was in his office at an automotive supply company when he got a call from the Detroit Police Department telling him to come to the station to be arrested., Related Classifications: Face Recognition)

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.
 

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.
 

biometrics, images, camera footage

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
AI technologies — like police facial recognition — discriminate against people of colour
+3
Wrongfully Accused by an Algorithm
Facial Recognition Blamed For False Arrest And Jail Time+1
Teaneck NJ bans facial recognition usage for police, citing bias
It's time to address facial recognition, the most troubling law enforcement AI toolHow Wrongful Arrests Based on AI Derailed 3 Men's Livestranslated-ja-Facial Recognition Led to Wrongful Arrests. So Detroit Is Making Changes. - レスポンス
AI technologies — like police facial recognition — discriminate against people of colour

AI technologies — like police facial recognition — discriminate against people of colour

theconversation.com

Wrongfully Accused by an Algorithm

Wrongfully Accused by an Algorithm

nytimes.com

'The Computer Got It Wrong': How Facial Recognition Led To False Arrest Of Black Man

'The Computer Got It Wrong': How Facial Recognition Led To False Arrest Of Black Man

npr.org

Detroit police admit to first facial recognition mistake after false arrest

Detroit police admit to first facial recognition mistake after false arrest

techrepublic.com

Detroit Police Chief: Facial Recognition Software Misidentifies 96% of the Time

Detroit Police Chief: Facial Recognition Software Misidentifies 96% of the Time

vice.com

Facial Recognition Blamed For False Arrest And Jail Time

Facial Recognition Blamed For False Arrest And Jail Time

silicon.co.uk

Teaneck NJ bans facial recognition usage for police, citing bias

Teaneck NJ bans facial recognition usage for police, citing bias

northjersey.com

Wrongfully arrested man sues Detroit police over false facial recognition match

Wrongfully arrested man sues Detroit police over false facial recognition match

washingtonpost.com

It's time to address facial recognition, the most troubling law enforcement AI tool

It's time to address facial recognition, the most troubling law enforcement AI tool

thebulletin.org

How Wrongful Arrests Based on AI Derailed 3 Men's Lives

How Wrongful Arrests Based on AI Derailed 3 Men's Lives

wired.com

translated-ja-Facial Recognition Led to Wrongful Arrests. So Detroit Is Making Changes.

translated-ja-Facial Recognition Led to Wrongful Arrests. So Detroit Is Making Changes.

nytimes.com

AI technologies — like police facial recognition — discriminate against people of colour
theconversation.com · 2020

Detroit police wrongfully arrested Robert Julian-Borchak Williams in January 2020 for a shoplifting incident that had taken place two years earlier. Even though Williams had nothing to do with the incident, facial recognition technology use…

Wrongfully Accused by an Algorithm
nytimes.com · 2020

"Note: In response to this article, the Wayne County prosecutor’s office said that Robert Julian-Borchak Williams could have the case and his fingerprint data expunged. “We apologize,” the prosecutor, Kym L. Worthy, said in a statement, add…

'The Computer Got It Wrong': How Facial Recognition Led To False Arrest Of Black Man
npr.org · 2020

Updated 9:05 p.m. ET Wednesday

Police in Detroit were trying to figure out who stole five watches from a Shinola retail store. Authorities say the thief took off with an estimated $3,800 worth of merchandise.

Investigators pulled a security…

Detroit police admit to first facial recognition mistake after false arrest
techrepublic.com · 2020

On Wednesday morning, the ACLU announced that it was filing a complaint against the Detroit Police Department on behalf of Robert Williams, a Black Michigan resident whom the group said is one of the first people falsely arrested due to fac…

Detroit Police Chief: Facial Recognition Software Misidentifies 96% of the Time
vice.com · 2020

Detroit police have used highly unreliable facial recognition technology almost exclusively against Black people so far in 2020, according to the Detroit Police Department’s own statistics. The department’s use of the technology gained nati…

Facial Recognition Blamed For False Arrest And Jail Time
silicon.co.uk · 2020

Racial bias and facial recognition. Black man in New Jersey arrested by police and spends ten days in jail after false face recognition match

Accuracy and racial bias concerns about facial recognition technology continue with the news of a …

Teaneck NJ bans facial recognition usage for police, citing bias
northjersey.com · 2021

Teaneck just banned facial recognition technology for police. Here's why

Show Caption Hide Caption Facial recognition program that works even if you’re wearing a mask A Japanese company says they’ve developed a system that can bypass face c…

Wrongfully arrested man sues Detroit police over false facial recognition match
washingtonpost.com · 2021

A Michigan man has sued Detroit police after he was wrongfully arrested and falsely identified as a shoplifting suspect by the department’s facial recognition software in one of the first lawsuits of its kind to call into question the contr…

It's time to address facial recognition, the most troubling law enforcement AI tool
thebulletin.org · 2021

Since a Minneapolis police officer killed George Floyd in March 2020 and re-ignited massive Black Lives Matter protests, communities across the country have been re-thinking law enforcement, from granular scrutiny of the ways that police us…

How Wrongful Arrests Based on AI Derailed 3 Men's Lives
wired.com · 2022

ROBERT WILLIAMS WAS doing yard work with his family one afternoon last August when his daughter Julia said they needed a family meeting immediately. Once everyone was inside the house, the 7-year-old girl closed all the blinds and curtains …

translated-ja-Facial Recognition Led to Wrongful Arrests. So Detroit Is Making Changes.
nytimes.com · 2024
自動翻訳済み
Kashmir Hillによるインシデント後のレスポンス

translated-ja-In January 2020, Robert Williams spent 30 hours in a Detroit jail because facial recognition technology suggested he was a criminal. The match was wrong, and Mr. Williams sued.

On Friday, as part of a legal settlement over his…

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

Police Departments Reported ShotSpotter as Unreliable and Wasteful

Gunshot detection system gets mixed reviews nationally

Oct 2012 · 15 レポート
Predictive Policing Program by Florida Sheriff’s Office Allegedly Violated Residents’ Rights and Targeted Children of Vulnerable Groups

Predictive policing strategies for children face pushback

Sep 2015 · 12 レポート
Predictive Policing Biases of PredPol

Policing the Future

Nov 2015 · 17 レポート

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