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Incident 535: COVID-19 Detection and Prognostication Models Allegedly Flagged for Methodological Flaws and Underlying Biases

Description: Peer-review of papers about COVID-19 detection and prognostication algorithms from 2020, including deployed models, revealed none to be ready for clinical use, due to methodological flaws and underlying biases such as lacking external validation or not specifying data sources and model training details.

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Alleged: unknown and Icahn School of Medicine researchers developed an AI system deployed by Mount Sinai Hospital and unknown, which harmed COVID-19 patients and COVID-19 healthcare providers.

Incident Stats

Incident ID
535
Report Count
2
Incident Date
2020-01-01
Editors
Khoa Lam
Applied Taxonomies
MIT

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
 

7.3. Lack of capability or robustness

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. AI system safety, failures, and limitations

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
 

Unintentional

Incident Reports

Reports Timeline

Incident OccurrenceCommon pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scansMachine learning is booming in medicine. It's also facing a credibility crisis
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

nature.com

Machine learning is booming in medicine. It's also facing a credibility crisis

Machine learning is booming in medicine. It's also facing a credibility crisis

statnews.com

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
nature.com · 2021

Abstract

Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many a…

Machine learning is booming in medicine. It's also facing a credibility crisis
statnews.com · 2021

The mad dash accelerated as quickly as the pandemic. Researchers sprinted to see whether artificial intelligence could unravel Covid-19's many secrets — and for good reason. There was a shortage of tests and treatments for a skyrocketing nu…

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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