Healthcare professionals are still cautious regarding Artificial Intelligence, mainly because of the opaque nature of certain models. These so-called “black box” models are too complex for humans to understand easily.
The recent surge of research into explainable AI is an effort to assuage the doubts arising due to the lack of interpretability in Artificial Intelligence, particularly regarding healthcare, where decisions can be a matter of life or death.
The potential of explainable machine learning is great. Still, those who utilize it in clinical decision-support tools or new research papers must critically comprehend its advantages and drawbacks.
This session will consider the main ideas and approaches of explainable machine learning in healthcare. The topics discussed involve distinguishing between interpretability and explainability and global versus local explanations.
Examples of strategies exhibited are permutation importance, surrogate decision trees, local interpretable model-agnostic explanations, and partial dependence graphs.
We will explore the restrictions of explainability techniques, emphasizing that they may overlook essential details concerning how black-box models operate. To finish, we recommend utilizing black-box models with explanations instead of interpretable approaches when suitable.
While opening the black box of AI systems has the potential to enhance transparency, accountability, and ethical decision-making, it also has limitations due to the inherent complexity and opacity of some AI models.
Striking a balance between transparency and practicality is essential in navigating the promise and limitations of opening the black box in AI. It requires interdisciplinary collaboration among researchers, practitioners, policymakers, and other stakeholders to ensure responsible and ethical development and use of AI systems that are both transparent and effective in addressing societal challenges.