Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers
Description
A study by the University of Toronto, the Vector Institute, and MIT showed the input databases that trained AI systems used to classify chest X-rays led the systems to show gender, socioeconomic, and racial biases.
Actors
Alleged Deployers
- mount-sinai-hospitals
Alleged Developers
- qure.ai
- darwinai
- aidoc
Harmed Parties
- economically-vulnerable-patients
- hispanic-patients
- women-and-girls
- patients
- medicaid-beneficiaries
- women
Classification
Risk Domain
Discrimination & ToxicityRisk Subdomain
1.3. Unequal performance across groups
MIT Risk Profile
Topics
Source
Related Incidents (Discrimination & Toxicity)
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Mitigation Tools
Tools related to this risk domain, via shared responsible AI topics.
Open Source
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A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
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A Python package to assess and improve fairness of machine learning models.
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responsible-ai-toolbox
microsoft/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.