Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks

In brief

This review examines explainable AI (XAI) in pediatric surgery, addressing critical challenges of algorithmic bias, transparency, and trust in AI-driven clinical decisions for vulnerable pediatric populations. The authors emphasize the need for ethical frameworks and standardized benchmarks to ensure safe, fair AI implementation in children's healthcare.

• XAI makes AI decisions interpretable and accountable, addressing opacity concerns in pediatric healthcare applications.
• Bias in AI models poses significant risks to vulnerable pediatric populations requiring careful ethical oversight.
• Standardized benchmarks are essential for evaluating XAI safety and effectiveness in pediatric surgery contexts.
• Trust and transparency in AI systems depend on explainability frameworks tailored to clinical decision-making needs.
• Ethical frameworks must guide XAI development to ensure fair and safe AI applications for children.

Written by the GCMD Library team from the article.