Incident 42: Inefficiencies in the United States Resident Matching Program

Description: Alvin Roth, a Ph.D at the University of Pittsburgh, describes the National Resident Matching Program (NRMP) and suggests future changes that are needed in the algorithm used to match recently graduated medical students to their residency programs.

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Alleged: National Resident Matching Program developed and deployed an AI system, which harmed Medical Residents.

Incident Stats

Incident ID
42
Report Count
2
Incident Date
1996-04-03
Editors
Sean McGregor

CSETv0 Taxonomy Classifications

Taxonomy Details

Full Description

Alvin Roth, a Ph.D at the University of Pittsburgh, describes the National Resident Matching Program (NRMP) and suggests future changes that are needed in the algorithm used to match recently graduated medical students to their residency programs.

Short Description

Alvin Roth, a Ph.D at the University of Pittsburgh, describes the National Resident Matching Program (NRMP) and suggests future changes that are needed in the algorithm used to match recently graduated medical students to their residency programs.

Severity

Negligible

Harm Distribution Basis

Other:Medical doctors matching to residency

System Developer

National Resident Matching Program

Sector of Deployment

Human health and social work activities

Location

United States

Named Entities

National Residential Matching Program, University of Pittsburgh, Alvin Roth

Technology Purveyor

National Residential Matching Program

Beginning Date

1996-04-03

Ending Date

1996-04-03

Near Miss

Unclear/unknown

Intent

Unclear

Lives Lost

No

Infrastructure Sectors

Healthcare and public health

CSETv1 Taxonomy Classifications

Taxonomy Details
stanford.edu · 1996

The National Residency Matching Program as a Labor Market

[Pulse: Communication]

Roth, Alvin E. PhD

Mellon Professor of Economics, Department of Economics, University of Pittsburgh.

Graphics Table 1

Although medical students are unaccustome…

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Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.

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