#explainable AI

'A practical tutorial showing how to build a robust ML pipeline for the diabetes dataset, evaluate and interpret it with permutation importance and PDP, and use Gemini as an AI collaborator for summaries, risks, and EDA snippets.'

Standard XAI methods fail to capture the hierarchical, precedent-driven nature of legal reasoning. This piece proposes a hybrid XAI design combining formal argumentation and LLM-generated narratives to produce legally defensible explanations.

'Healthcare providers want pragmatic AI that fixes staffing and workflow problems, proves results in real settings, integrates smoothly, and meets compliance requirements.'

Discover how explainable AI addresses unpredictability in AI systems, fostering trust and accountability while enabling businesses to transform operations with transparent, reliable processes.