Open your black box classifier.

Area of Science:

• Computer Science
• Artificial Intelligence
• Healthcare Technology

Background:

• Machine learning models are increasingly used in high-stakes fields like healthcare.
• Interpreting individual predictions from these models is crucial for end-user trust and adoption.
• Many current machine learning models function as "black boxes," hindering interpretability.

Purpose of the Study:

• To outline recent advancements in interpretable machine learning classifiers.
• To discuss methods for increasing the transparency of "black box" models.
• To highlight the importance of interpretability for machine learning in healthcare.

Main Methods:

• Review of recent developments in interpretable classification techniques.
• Discussion of methodologies aimed at opening "black box" models.
• Synthesis of current research trends in explainable AI (XAI).

Main Results:

• Several novel interpretable classifiers have emerged.
• New methods facilitate the explanation of complex model decisions.
• Progress has been made in making machine learning predictions more transparent.

Conclusions:

• Enhancing the interpretability of machine learning is a key research priority.
• Accessible explanations of AI predictions are vital for healthcare applications.
• Continued development of explainable AI methods will foster trust and utility.