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Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support.

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User interface design significantly impacts artificial intelligence (AI) bias in clinical decision support (CDS). Optimizing UI design is crucial for mitigating AI bias and enhancing CDS safety and effectiveness.

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Area of Science:

  • Health Informatics
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Bias in artificial intelligence (AI) algorithms is a significant concern in clinical decision support (CDS).
  • Current discussions on AI bias primarily focus on data quality and algorithm design.
  • The influence of user interface (UI) design on user behavior and AI application outcomes is often underestimated.

Purpose of the Study:

  • To emphasize the critical, yet often overlooked, role of UI design in mitigating bias within AI-based CDS.
  • To explore the interdependency between AI algorithm development and UI design.
  • To propose strategies for enhancing the safety and efficacy of CDS through improved UI design.

Main Methods:

  • This perspective paper reviews existing literature on design's influence on user behavior.
  • It discusses the relationship between AI algorithm development and UI design.
  • It provides an example of UI design's role in manifesting bias in machine learning-based CDS.

Main Results:

  • The impact of design on user behavior is well-established in fields like behavioral economics.
  • UI design choices can directly influence how biases present themselves in AI-based CDS.
  • Specific UI design elements can inadvertently reinforce or mitigate algorithmic limitations.

Conclusions:

  • UI design is a critical factor in addressing bias in AI-based CDS, alongside data and algorithms.
  • Human factors methods should be employed to identify and rectify UI-related issues before CDS deployment.
  • Effective risk communication strategies are essential for managing user perceptions and mitigating bias.