Explainable AI
AI systems designed to provide clear explanations for their decisions, enabling humans to understand and trust automated choices.
Definition
Explainable AI (XAI) creates models that can articulate their reasoning process, showing which factors influenced specific decisions and how different inputs contributed to outputs. This transparency is crucial for trust and accountability.
XAI techniques range from simple feature importance scores to complex visualizations that highlight decision paths. The goal is making AI systems interpretable to domain experts and stakeholders who need to understand automated decisions.
Why It Matters
Regulatory requirements increasingly demand AI transparency, particularly in high-stakes decisions like lending, hiring, and healthcare. Organizations need explainable systems to demonstrate compliance and fairness.
Beyond compliance, explainable AI builds stakeholder trust and enables better human-AI collaboration by helping users understand when and why to rely on AI recommendations versus human judgment.
Examples in Practice
Banks use explainable AI for loan approval systems, providing clear reasons for credit decisions that satisfy regulatory requirements and help customers understand outcomes.
Healthcare providers employ explainable diagnostic AI that highlights specific image regions or symptoms that led to medical recommendations, enabling doctors to validate and trust AI insights.
HR departments implement explainable AI for resume screening that can articulate which qualifications and experiences influenced candidate rankings, ensuring fair and defensible hiring processes.