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Bioethics Principles in Machine Learning-Healthcare Application Design: Achieving Health Justice and Health Equity.

Roschelle L Fritz1, Connie Kim Yen Nguyen-Truong2, Thomas May3

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Summary
This summary is machine-generated.

This course addresses bias in healthcare artificial intelligence (AI) by operationalizing bioethics principles like autonomy. It supports developers in creating equitable AI by integrating diverse perspectives and human-centered design.

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Area of Science:

  • Health Informatics
  • Bioethics
  • Artificial Intelligence

Background:

  • Healthcare artificial intelligence (AI) is rapidly advancing, yet biases towards underrepresented groups are a growing concern.
  • There is a recognized need to mitigate bias in healthcare AI, but practical guidance on implementing bioethics principles, such as autonomy, in AI design is lacking.
  • Existing approaches often overlook the social determinants of health, health justice, and health equity.

Purpose of the Study:

  • To provide computer scientists and healthcare professionals with actionable strategies for building and deploying ethical healthcare AI.
  • To operationalize bioethics principles, specifically autonomy, within the product design and implementation phases of healthcare AI.
  • To frame AI development through the lens of Social Determinants of Health, health justice, and health equity.

Main Methods:

  • Introduction to the bioethics principle of autonomy within human-centered design frameworks (Module 1).
  • Exploration of design thinking models and presentation of four activities for embedding ethical principles during the AI design process (Module 2).
  • Emphasis on the critical role of incorporating diverse perspectives to minimize harm and uphold the values of underrepresented populations (Module 3).

Main Results:

  • The course offers a structured approach to integrating ethical considerations into healthcare AI development.
  • It provides practical tools and methodologies for designers and developers to address bias proactively.
  • It highlights the importance of inclusivity and diverse input for achieving health equity and justice in AI.

Conclusions:

  • Ethical considerations, particularly autonomy and inclusivity, must be embedded throughout the healthcare AI lifecycle.
  • A Social Determinants of Health approach is crucial for developing equitable and just healthcare AI.
  • This course equips professionals with the knowledge to mitigate bias and promote health equity in AI technologies.