Explainable AI for Bioinformatics: Methods, Tools, and Applications

Healthcare AI requires explainability by law: The paper emphasizes that in sensitive healthcare applications, explainability and accountability are "not only desirable but also legally required" for AI systems that impact human lives, making transparency a compliance issue rather than just a best practice.

Black-box AI models pose real risks in bioinformatics: Complex deep neural networks used in biomedical applications are often "opaque" and difficult to understand, creating challenges for both end-users and decision-makers who need to trust and verify AI recommendations in clinical settings.

Fairness and bias are critical concerns: The paper identifies algorithmic bias and discrimination as growing concerns, noting that "algorithmic decisions should not show bias or discrimination towards certain groups or individuals based on sensitive attributes"—a particular risk when AI systems are deployed across diverse patient populations.

Generic AI explanation methods don't work well for biology: Most existing explainability techniques come from other fields like computer vision and require "customization and domain-specific adaptation" to work effectively in bioinformatics, meaning off-the-shelf solutions are insufficient for healthcare applications.

Interpretable models bridge the transparency gap: The paper advocates for interpretable machine learning approaches that can "explain how they make predictions and the factors that influence their outcomes," offering a path toward AI systems that are both accurate and trustworthy in medical decision-making.