Certified BTG AEGIS AI™ Crash Course – Ethical Artificial Intelligence (2025 Edition)

The BTG AEGIS AI Crash Course – Ethical Artificial Intelligence (2025 Edition) provides a structured, practical framework for building and governing responsible AI systems. Recognised under CERN Zenodo and OpenAIRE EU, this program combines international AI ethics guidelines — including the EU AI Act, UNESCO’s Recommendation on the Ethics of AI, and IEEE’s Ethically Aligned Design — to train learners in identifying, mitigating, and managing ethical risks in artificial intelligence. Participants explore real-world issues of bias, transparency, accountability, and human oversight while learning to design AI governance frameworks and ethical audit processes. The course culminates in an Ethical AI Design Proposal, preparing graduates to act as certified BTG AEGIS Ethical AI Practitioners capable of integrating fairness and compliance into innovation. Author: Santosh Sunar – Founder, BeTheGuide® | UZB BUZ AI | ExoAnsion Pvt Ltd (India) Community: BTG AEGIS AI – Global Ethical AI Research Community Recognition: CERN Zenodo & OpenAIRE EU Background & References: This crash course builds upon the author’s earlier research in the BTG AEGIS AI Series, both published and recognised under CERN Zenodo and OpenAIRE EU: 1. BTG AEGIS AI – The Ethical AI-as-a-Service Framework for Global Governance (Zenodo DOI: https://doi.org/10.5281/zenodo.17459356) 2. BTG AEGIS AI – Prototype Edition Unveiling the Prototype https://doi.org/10.5281/zenodo.17505830 🔗 Related Research Works This educational publication is developed under the BTG AEGIS AI Open Science Series (CERN Zenodo & OpenAIRE EU). It operationalises the principles defined in the BTG AEGIS AI Framework and integrates the model introduced in the Prototype Edition. Related records: 🧭 BTG AEGIS AI Framework – The Ethical AI-as-a-Service Model — https://doi.org/10.5281/zenodo.17459356 (Parent Framework – isPartOf) ⚙️ BTG AEGIS AI Prototype Edition (Diagram) — https://doi.org/10.5281/zenodo.17505830 (Referenced visual model)