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インシデント 79: Kidney Testing Method Allegedly Underestimated Risk of Black Patients

概要: Decades-long use of the estimated glomerular filtration rate (eGFR) method to test kidney function which considers race has been criticized by physicians and medical students for its racist history and inaccuracy against Black patients.

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組織

すべての組織を表示
推定: Chronic Kidney Disease Epidemiology Collaborationが開発し提供したAIシステムで、Black patients と African-American patients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
79
レポート数
3
インシデント発生日
1999-03-16
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.
 

79

AI Tangible Harm Level Notes

Notes about the AI tangible harm level assessment
 

There is no AI. The harm comes from a formula that uses race as a factor.

Notes (special interest intangible harm)

Input any notes that may help explain your answers.
 

4.1 - Black patients overlooked by the calculation because of built-in points had their access to critical public healthcare reduced.

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

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.
 

Unknown/unclear

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.
 

Low

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.
 

creatinine levels, age, sex, race

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
 

Human

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 OccurrenceA yearslong push to remove racist bias from kidney testing gains new ground+1
Examining the Potential Impact of Race Multiplier Utilization in Estimated Glomerular Filtration Rate Calculation on African-American Care Outcomes
A yearslong push to remove racist bias from kidney testing gains new ground

A yearslong push to remove racist bias from kidney testing gains new ground

statnews.com

Examining the Potential Impact of Race Multiplier Utilization in Estimated Glomerular Filtration Rate Calculation on African-American Care Outcomes

Examining the Potential Impact of Race Multiplier Utilization in Estimated Glomerular Filtration Rate Calculation on African-American Care Outcomes

link.springer.com

How an Algorithm Blocked Kidney Transplants to Black Patients

How an Algorithm Blocked Kidney Transplants to Black Patients

wired.com

A yearslong push to remove racist bias from kidney testing gains new ground
statnews.com · 2020

For years, physicians and medical students, many of them Black, have warned that the most widely used kidney test — the results of which are based on race — is racist and dangerously inaccurate. Their appeals are gaining new traction, with …

Examining the Potential Impact of Race Multiplier Utilization in Estimated Glomerular Filtration Rate Calculation on African-American Care Outcomes
link.springer.com · 2020

BACKGROUND: Advancing health equity entails reducing disparities in care. African-American patients with chronic kidney disease (CKD) have poorer outcomes, including dialysis access placement and transplantation. Estimated glomerular filtra…

How an Algorithm Blocked Kidney Transplants to Black Patients
wired.com · 2020

BLACK PEOPLE IN the US suffer more from chronic diseases and receive inferior health care relative to white people. Racially skewed math can make the problem worse.

Doctors often make life-changing decisions about patient care based on algo…

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

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
Justice Department’s Recidivism Risk Algorithm PATTERN Allegedly Caused Persistent Disparities Along Racial Lines

Flaws plague a tool meant to help low-risk federal prisoners win early release

Jan 2022 · 1 レポート
Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers

Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers

Oct 2020 · 1 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

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
Justice Department’s Recidivism Risk Algorithm PATTERN Allegedly Caused Persistent Disparities Along Racial Lines

Flaws plague a tool meant to help low-risk federal prisoners win early release

Jan 2022 · 1 レポート
Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers

Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers

Oct 2020 · 1 レポート

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