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Updated: Apr 28, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

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Auditing fairness in clinical AI systems using provenance-based simulation: a comparative and regulatory perspective.

Fidelis Alu1, Sunkanmi Oluwadare1, Nnennaya Ngwanma Halliday1

  • 1School of Information Technology, University of Cincinnati, Cincinnati, OH, United States.

Frontiers in Artificial Intelligence
|April 27, 2026
PubMed
Summary

An AI auditing framework using data provenance successfully detected gender bias in clinical decision support models. This approach helps ensure fairness and regulatory compliance for responsible AI in healthcare.

Keywords:
algorithmic fairnessbias detectionclinical AIexplainable AImodel governanceprovenance auditability

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Last Updated: Apr 28, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Health Equity

Background:

  • Algorithmic bias and lack of transparency hinder Artificial Intelligence (AI) adoption in clinical decision support.
  • Data provenance is crucial for auditing AI fairness and ensuring trustworthy AI systems.

Purpose of the Study:

  • To develop and evaluate an AI auditing framework using detailed data provenance to assess fairness in clinical decision support models.
  • To compare the fairness of logistic regression and random forest models using a synthetic patient dataset.

Main Methods:

  • Simulated audits on a synthetic patient dataset (N=1,000) using logistic regression and random forest models.
  • Employed 5-fold cross-validation and permutation testing to detect gender bias.
  • Utilized detailed data provenance logs to trace and quantify bias.

Main Results:

  • Provenance logs successfully detected gender biases in both logistic regression and random forest models.
  • Logistic regression showed statistically significant bias (EOD = +0.256, p = 0.0080), while random forest's bias was not statistically significant (EOD = +0.055, p = 0.5664).
  • Random forest demonstrated 57% less bias than logistic regression, despite lower overall accuracy.

Conclusions:

  • The developed provenance-based auditing framework effectively distinguishes systematic discrimination from random variation in AI models.
  • A standardized AI Fairness Provenance Record supports regulatory compliance (FDA, ONC) and promotes responsible AI in clinical settings.
  • Fairness in AI is not solely dependent on model interpretability; auditing with data provenance is essential.