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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.

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  • 1Duke University.

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This summary is machine-generated.

Explainable AI (Artificial Intelligence) methods for black box models can cause harm. Designing inherently interpretable machine learning models is crucial for high-stakes decisions in healthcare and criminal justice.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Black box machine learning models are increasingly used in high-stakes societal domains like healthcare and criminal justice.
  • Current methods focus on explaining these models, which may perpetuate harmful practices.
  • The limitations of explainable AI (XAI) for opaque models are a growing concern.

Purpose of the Study:

  • To highlight the critical difference between explaining black box models and developing inherently interpretable models.
  • To outline reasons why relying on explainable black boxes is detrimental for critical decision-making.
  • To identify challenges and propose solutions for advancing interpretable machine learning.

Main Methods:

  • Conceptual analysis of explainable AI versus interpretable AI.
  • Review of existing literature on the risks of black box models.
  • Identification of key challenges in developing interpretable machine learning.

Main Results:

  • Explaining black box models is insufficient and potentially harmful for high-stakes applications.
  • Inherently interpretable models offer a safer and more reliable alternative.
  • Challenges in interpretable machine learning include model complexity and scalability.

Conclusions:

  • Prioritizing the development and adoption of inherently interpretable machine learning models is essential.
  • Shifting focus from post-hoc explanations to interpretable-by-design approaches mitigates risks in critical domains.
  • Interpretable models can be effectively applied in criminal justice, healthcare, and computer vision.