AI incident ·
Inefficiencies in the United States Resident Matching Program
In brief
An AI system built and deployed by National Resident Matching Program allegedly harmed Medical Residents.
- Risk domain
- Socioeconomic & Environmental Harms
- Occurred
- Coverage
- 2 reports
What happened
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.
Laws that address this harm
Policy angle: Classified under Socioeconomic & Environmental Harms (Competitive dynamics) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- Colorado AI Act
- Framework Convention on AI (CETS 225)
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (1)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- National Resident Matching Program
- Alleged developer
- National Resident Matching Program
- Alleged harmed party
- Medical Residents
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Socioeconomic & Environmental Harms
- Risk subdomain
- 6.4 Competitive dynamics
- Causal entity
- Other
- Intent
- Other
- Timing
- Other
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 6.4.
- Increased competition
"Increased competition - The inappropriate or unethical use of technology to gain market share."
- Geopolitical risk
"As AI is increasingly seen as a powerful technology, countries are racing to develop it ahead of their geopolitical rivals, a competition that could lead to geopolitical tensions [138], [139]... The emphasis of this ris...
- Worsened conflict
"Cooperation and conflict: we’re seeing more focus and investment on the kinds of AI capabilities that make conflict more likely and severe, rather than those likely to improve cooperation. So, on our current trajectory,...
- Resource conflicts driven by AI development
"AI development may itself become a new flash point for conflicts—causing more conflict to occur— especially conflicts over AI-relevant resources (such as data centres, semiconductor manufacturing facilities and raw mate...
- Type 4: Willful indifference
As a side effect of a primary goal like profit or influence, AI creators can willfully allow it to cause widespread societal harms like pollution, resource depletion, mental illness, misinformation, or injustice.
- Opacity (industry opacity)
"Opacity is not solely due to the technological complexity that limits developers’ and users’ understanding of how generative models function on a technical level. It is further exacerbated by the practices of organizati...
- Competitive pressures in GPAI product release
"In competitive situations, developers of general-purpose AI systems might cut corners on the safety evaluation of their GPAI model and instead spend more time and effort on the capabilities of those systems [183, 69]. T...
- Corporate AI Race
"Although competition between companies can be beneficial, creating more useful products for consumers, there are also pitfalls. First, the benefits of economic activity may be unevenly distributed, incentivizing those w...
Incidents in the same risk subdomain
- Korean Internet Portal Giant Naver Manipulated Shopping and Video Search Algorithms to Favor In-House Services
- Apple Tweaked App Store Ranking Algorithms, Allegedly Resulted in Demotion of Local Apps in China
Source record: incident #42 on the AI Incident Database · all 2 reports