AI Ethics & Safety Literacy

Ethics isn't a department. It's a checklist that shows up everywhere AI does. The work covers principles, frameworks, and the practical skills that turn 'we should be careful' into something you can actually audit. Specific territory: algorithmic bias detection and mitigation, hallucination assessment, privacy-preserving AI techniques, transparency and explainability requirements, informed consent for AI-processed data, and regulatory compliance across multiple jurisdictions at once. Day to day, this means running AI impact assessments, designing fairness metrics, and building governance structures so responsible deployment is the default and not the exception.