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Ethical Issues01:27

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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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Enhancing fairness in AI-enabled medical systems with the attribute neutral framework.

Lianting Hu1,2,3,4, Dantong Li2,3,4, Huazhang Liu3,4

  • 1The Data Center, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430016, Hubei, China.

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This study introduces the Attribute Neutral Framework to reduce bias in artificial intelligence (AI) for healthcare. The Attribute Neutralizer (AttrNzr) improves AI fairness across diverse groups while maintaining diagnostic accuracy.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Healthcare Equity
  • Machine Learning for Healthcare

Background:

  • Artificial intelligence (AI) in healthcare faces challenges with unfairness and inequity.
  • Biased AI models may learn undesirable correlations between sensitive attributes and health data.
  • Unequal AI performance across protected groups hinders equitable healthcare deployment.

Purpose of the Study:

  • To introduce a novel framework for disentangling and neutralizing biased attributes in AI healthcare models.
  • To develop a method that improves representation and fairness across diverse patient subgroups.
  • To mitigate unfairness in AI diagnostic models without compromising performance.

Main Methods:

  • Introduction of the Attribute Neutral Framework and the Attribute Neutralizer (AttrNzr).
  • Generation of neutralized data where protected attributes are not easily predictable.
  • Training of a disease diagnosis model (DDM) using neutralized data.

Main Results:

  • AttrNzr effectively reduces unfairness in the DDM compared to other mitigation algorithms.
  • The Attribute Neutral Framework maintains the overall disease diagnosis performance of the DDM.
  • AttrNzr demonstrates utility in neutralizing multiple attributes and during the training phase only.

Conclusions:

  • The Attribute Neutral Framework offers a data-centered, model-independent solution to AI fairness challenges in medicine.
  • AttrNzr addresses the root cause of unfairness by neutralizing biased data.
  • This approach has significant potential for creating more equitable AI-enabled medical systems.