Description: A study published in JAMA Network Open reveals that racial bias built into a commonly used medical diagnostic algorithm for lung function may be leading to underdiagnoses of breathing problems in Black men. The study suggests that as many as 40% more Black male patients might have been accurately diagnosed if the software were not racially biased. The software algorithm adjusts diagnostic thresholds based on race, affecting medical treatments and interventions.
Entities
View all entitiesAlleged: unknown developed an AI system deployed by University of Pennsylvania Health System, which harmed Black men who underwent lung function tests between 2010 and 2020 and potentially received inaccurate or delayed diagnoses and medical interventions due to the biased algorithm.
Incident Stats
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.
- 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
Incident Reports
Reports Timeline
NEW YORK (AP) — Racial bias built into a common medical test for lung function is likely leading to fewer Black patients getting care for breathing problems, a study published Thursday suggests.
As many as 40% more Black male patients in th…
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.