Skip to Content
logologo
AI Incident Database
Open TwitterOpen RSS FeedOpen FacebookOpen LinkedInOpen GitHub
Open Menu
発見する
投稿する
  • ようこそAIIDへ
  • インシデントを発見
  • 空間ビュー
  • テーブル表示
  • リスト表示
  • 組織
  • 分類法
  • インシデントレポートを投稿
  • 投稿ランキング
  • ブログ
  • AIニュースダイジェスト
  • リスクチェックリスト
  • おまかせ表示
  • サインアップ
閉じる
発見する
投稿する
  • ようこそAIIDへ
  • インシデントを発見
  • 空間ビュー
  • テーブル表示
  • リスト表示
  • 組織
  • 分類法
  • インシデントレポートを投稿
  • 投稿ランキング
  • ブログ
  • AIニュースダイジェスト
  • リスクチェックリスト
  • おまかせ表示
  • サインアップ
閉じる

インシデント 461: IRS Audited Black Taxpayers More Frequently Reportedly Due to Algorithm

概要: The IRS was auditing Black taxpayers more frequently than other groups allegedly due to the design of their algorithms, focusing on easier-to-conduct audits which inadvertently correlated with the group's pattern of tax filing errors.

ツール

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: Internal Revenue Serviceが開発し提供したAIシステムで、Black taxpayers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
461
レポート数
4
インシデント発生日
2008-07-18
エディタ
Khoa Lam
Applied Taxonomies
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.
 

461

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
 

2023

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
 

01

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
 

Estimated Date

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

No

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+3
Measuring and Mitigating Racial Disparities in Tax Audits
Measuring and Mitigating Racial Disparities in Tax Audits

Measuring and Mitigating Racial Disparities in Tax Audits

siepr.stanford.edu

Black Americans Are Much More Likely to Face Tax Audits, Study Finds

Black Americans Are Much More Likely to Face Tax Audits, Study Finds

nytimes.com

Black taxpayers more than three times more likely to be audited by IRS

Black taxpayers more than three times more likely to be audited by IRS

thehill.com

IRS Disproportionately Audits Black Taxpayers

IRS Disproportionately Audits Black Taxpayers

hai.stanford.edu

Measuring and Mitigating Racial Disparities in Tax Audits
siepr.stanford.edu · 2023

Government agencies around the world use data-driven algorithms to allocate enforcement resources. Even when such algorithms are formally neutral with respect to protected characteristics like race, there is widespread concern that they can…

Black Americans Are Much More Likely to Face Tax Audits, Study Finds
nytimes.com · 2023

WASHINGTON — Black taxpayers are at least three times as likely to be audited by the Internal Revenue Service as other taxpayers, even after accounting for the differences in the types of returns each group is most likely to file, a team of…

Black taxpayers more than three times more likely to be audited by IRS
thehill.com · 2023

A new report published Monday found that the IRS audits Black taxpayers at a significantly higher rate than non-Black taxpayers.

The paper, published by Stanford’s Institute for Economic Policy Research, said that despite the IRS’s “race-bl…

IRS Disproportionately Audits Black Taxpayers
hai.stanford.edu · 2023

Researchers have long wondered if the IRS uses its audit powers equitably. And now we have learned that it does not.

Black taxpayers receive IRS audit notices at least 2.9 times (and perhaps as much as 4.7 times) more often than non-Black t…

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

COMPAS Algorithm Performs Poorly in Crime Recidivism Prediction

A Popular Algorithm Is No Better at Predicting Crimes Than Random People

May 2016 · 22 レポート
Northpointe Risk Models

Machine Bias - ProPublica

May 2016 · 15 レポート
Algorithmic Health Risk Scores Underestimated Black Patients’ Needs

A Health Care Algorithm Offered Less Care to Black Patients

Oct 2019 · 7 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

COMPAS Algorithm Performs Poorly in Crime Recidivism Prediction

A Popular Algorithm Is No Better at Predicting Crimes Than Random People

May 2016 · 22 レポート
Northpointe Risk Models

Machine Bias - ProPublica

May 2016 · 15 レポート
Algorithmic Health Risk Scores Underestimated Black Patients’ Needs

A Health Care Algorithm Offered Less Care to Black Patients

Oct 2019 · 7 レポート

リサーチ

  • “AIインシデント”の定義
  • “AIインシデントレスポンス”の定義
  • データベースのロードマップ
  • 関連研究
  • 全データベースのダウンロード

プロジェクトとコミュニティ

  • AIIDについて
  • コンタクトとフォロー
  • アプリと要約
  • エディタのためのガイド

インシデント

  • 全インシデントの一覧
  • フラグの立ったインシデント
  • 登録待ち一覧
  • クラスごとの表示
  • 分類法

2024 - AI Incident Database

  • 利用規約
  • プライバシーポリシー
  • Open twitterOpen githubOpen rssOpen facebookOpen linkedin
  • 1420c8e