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Incident #535·AI Safety & Failures

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

January 1, 20202 reportsView on AIID

Explainability

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.

Actors

Alleged Deployers

mount-sinai-hospital
unknown

Alleged Developers

icahn-school-of-medicine-researchers
unknown

Harmed Parties

covid-19-patients
covid-19-healthcare-providers

Classification

Risk Domain

AI Safety & Failures

Risk Subdomain

7.3. Lack of capability or robustness

MIT Risk Profile

HumanUnintentionalPost-deployment

Topics

Explainability

Source

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Research Papers on This Topic

Concrete Problems in AI Safety

3,612 citationsJun 2016

A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI

2,338 citationsJul 2019

Representation Engineering: A Top-Down Approach to AI Transparency

1,499 citationsOct 2023

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

1,178 citationsJul 2021

Mitigation Tools

Tools related to this risk domain, via shared responsible AI topics.

Open Source

lime

marcotcr/lime

Lime: Explaining the predictions of any machine learning classifier

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facets

PAIR-code/facets

Visualizations for machine learning datasets

⭐ 7,374

interpret

interpretml/interpret

Fit interpretable models. Explain blackbox machine learning.

⭐ 6,788

captum

Source Reports(2 total)

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

statnews.com· Jun 2021

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

nature.com· Mar 2021

AI Incident Database (AIID), MIT AI Risk Initiative. Data from the AI Incident Database (AIID), MIT AI Risk Initiative. Licensed under CC BY-SA 4.0.

Databaseaiid

LicenseCC BY-SA 4.0

Dec 2015

pytorch/captum

Model interpretability and understanding for PyTorch

⭐ 5,548

Commercial

Datomize

Synthetic data generation for privacy compliance

Protect AI

Helping organizations build and use AI safely.

Censius

AI observability for model governance.

HiddenLayer

Model detection and response (MDR) for AI model protection.