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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
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

42

Estimated Date

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

No

Lives Lost

Indicates the number of deaths reported
 

0

Injuries

Indicate the number of injuries reported.
 

0

Estimated Harm Quantities

Indicates if the amount was estimated.
 

No

There is a potentially identifiable specific entity that experienced the harm

A potentially identifiable specific entity that experienced the harm can be characterized or identified.
 

No

CSETv0 Taxonomy Classifications

Taxonomy Details

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

Infrastructure Sectors

Where applicable, this field indicates if the incident caused harm to any of the economic sectors designated by the U.S. government as critical infrastructure.
 

Healthcare and public health

Lives Lost

Were human lives lost as a result of the incident?
 

No

Intent

Was the incident an accident, intentional, or is the intent unclear?
 

Unclear

Near Miss

Was harm caused, or was it a near miss?
 

Unclear/unknown

Ending Date

The date the incident ended.
 

1996-04-03

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

5.2. Loss of human agency and autonomy

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. Human-Computer Interaction

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
 

Pre-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Intentional

Incident Reports

Reports Timeline

+1
The National Residency Matching Program as a Labor Market
AI Incident Database Incidents Converted to Issues
The National Residency Matching Program as a Labor Market

The National Residency Matching Program as a Labor Market

stanford.edu

AI Incident Database Incidents Converted to Issues

AI Incident Database Incidents Converted to Issues

github.com

The National Residency Matching Program as a Labor Market
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…

AI Incident Database Incidents Converted to Issues
github.com · 2022

The following former incidents have been converted to "issues" following an update to the incident definition and ingestion criteria.

21: Tougher Turing Test Exposes Chatbots’ Stupidity

Description: The 2016 Winograd Schema Challenge highli…

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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