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インシデント 93: HUD charges Facebook with enabling housing discrimination

概要: In March 2019 the U.S. Department of Housing and Urban Development charged Facebook with violating the Fair Housing Act by allowing real estate sellers to target advertisements in a discriminatory manner.

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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: Facebookが開発し提供したAIシステムで、Facebook users of minority groups , non-American-born Facebook users , non-Christian Facebook users , Facebook users interested in accessibility と Facebook users interested in Hispanic culture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
93
レポート数
4
インシデント発生日
2018-08-13
エディタ
Sean McGregor, Khoa Lam
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.
 

93

Notes (special interest intangible harm)

Input any notes that may help explain your answers.
 

HUD alleged that Facebook restricted who saw ads based on users' age, gender, zip code, religion, citizenship, and more.

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
 

2019

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
 

03

Estimated Date

“Yes” if the data was estimated. “No” otherwise.
 

No

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.
 

user Facebook activity, user social network data

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

Incident Occurrence+1
HUD charges Facebook with enabling housing discrimination
HUD v. FacebookJustice Department Secures Groundbreaking Settlement Agreement with Meta Platforms, Formerly Known as Facebook, to Resolve Allegations of Discriminatory Advertising
HUD charges Facebook with enabling housing discrimination

HUD charges Facebook with enabling housing discrimination

thehill.com

HUD Is Suing Facebook For Housing Discrimination

HUD Is Suing Facebook For Housing Discrimination

forbes.com

HUD v. Facebook

HUD v. Facebook

nafcu.org

Justice Department Secures Groundbreaking Settlement Agreement with Meta Platforms, Formerly Known as Facebook, to Resolve Allegations of Discriminatory Advertising

Justice Department Secures Groundbreaking Settlement Agreement with Meta Platforms, Formerly Known as Facebook, to Resolve Allegations of Discriminatory Advertising

justice.gov

HUD charges Facebook with enabling housing discrimination
thehill.com · 2019

The Department of Housing and Urban Development (HUD) on Thursday charged Facebook with encouraging and enabling housing discrimination through its targeted advertising practices.

HUD is charging Facebook with violating the Fair Housing Act…

HUD Is Suing Facebook For Housing Discrimination
forbes.com · 2019

The federal government is suing Facebook over allegations of housing discrimination in the social network's advertising platform.

The U.S. Department of Housing and Urban Development on Thursday announced that it is charging Facebook with v…

HUD v. Facebook
nafcu.org · 2019

Last week I watched a webinar (additional cost) that is available on the NAFCU Online Training Center titled Red Flags for Fair Lending. The webinar was presented live in March of this year on the same day that the United States Department …

Justice Department Secures Groundbreaking Settlement Agreement with Meta Platforms, Formerly Known as Facebook, to Resolve Allegations of Discriminatory Advertising
justice.gov · 2022

The Department of Justice announced today that it has obtained a settlement agreement resolving allegations that Meta Platforms Inc., formerly known as Facebook Inc., has engaged in discriminatory advertising in violation of the Fair Housin…

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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Ever AI Reportedly Deceived Customers about FRT Use in App

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Airbnb's Trustworthiness Algorithm Allegedly Banned Users without Explanation, and Discriminated against Sex Workers

Is Airbnb using an algorithm to ban users from the platform?

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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

Ever AI Reportedly Deceived Customers about FRT Use in App

Millions of people uploaded photos to the Ever app. Then the company used them to develop facial recognition tools.

Apr 2019 · 7 レポート
Airbnb's Trustworthiness Algorithm Allegedly Banned Users without Explanation, and Discriminated against Sex Workers

Is Airbnb using an algorithm to ban users from the platform?

Jul 2017 · 6 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

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