Ethics and Governance of Generative AI
Generative Artificial Intelligence (Gen-AI)
COURSE OVERVIEW
In an era where AI is rapidly integrated into enterprise workflows, the ability to deploy models responsibly is as critical as the technology itself. This two-day program provides a deep dive into the moral and regulatory guardrails required to manage Google Gemini and other LLMs. Participants will move from theoretical ethics to practical governance, learning how to mitigate algorithmic bias, ensure data sovereignty on Google Cloud, and prepare for the 2026 global regulatory landscape.
COURSE OBJECTIVES
By the end of this course, participants will be able to:
- Identify Algorithmic Bias: Detect and mitigate prejudices within training data and model outputs.
- Implement Google’s AI Principles: Operationalize responsible AI within a corporate environment.
- Master Technical Transparency: Use Google’s Model Cards and Explainable AI (XAI) to demystify "Black Box" models.
- Navigate Compliance: Prepare for the EU AI Act and NIST standards using Google Cloud’s compliance tools.
- Establish Governance Structures: Design internal policies for human-in-the-loop oversight and risk management.
Duration: 2 Days / 16 Hours
Delivery Method: Classroom-based, Virtual Instructor Led Training
COURSE OUTLINE
Day 1: Ethical Foundations of AI
Focus: Building a moral compass for GenAI deployment.
- Introduction to AI Ethics: Defining the ethical stakes of Generative AI; why LLMs require unique moral scrutiny.
- The Problem of Bias & Fairness:
- Identifying historical bias in training datasets.
- Strategies for achieving "Fairness by Design" in Google Gemini applications.
- Transparency & Explainability:
- Bridging the gap between complex neural networks and stakeholder understanding.
- Using Google’s Interpretability Tools to explain model decision-making.
- Responsible AI Principles: Deep dive into Google’s 7 AI Principles—from "Socially Beneficial" to "Privacy-Forward."
- Data Privacy & Sovereign AI:
- Managing PII (Personally Identifiable Information) during model fine-tuning.
- The role of Confidential Computing in protecting sensitive datasets.
- Societal Impacts: Analyzing the effect of AI on labor, misinformation (Deepfakes), and digital equity.
- Hands-on Workshop: Developing an Ethical Decision-Making Framework for a high-stakes AI use case (e.g., Automated Loan Approvals).
Day 2: AI Governance and Compliance
Focus: Turning ethics into enforceable organizational policy.
- AI Governance Frameworks: Introduction to the Google Secure AI Framework (SAIF) and its application in 2026.
- Regulatory Landscapes:
- Navigating the EU AI Act and emerging global AI mandates.
- Preparing for mandatory audits and technical documentation requirements.
- AI Risk Management:
- Conducting Impact Assessments for Generative AI projects.
- Red-teaming models to discover vulnerabilities and "hallucination" triggers.
- Security, Trust, & Watermarking:
- Leveraging SynthID for content authenticity.
- Ensuring trust through model versioning and lineage tracking.
- Organizational Models: Establishing an "AI Ethics Board" and defining the roles of the Chief AI Officer vs. Compliance.
- Human Oversight: Designing effective "Human-in-the-loop" (HITL) workflows to prevent autonomous model failure.
- Building Responsible AI Policies: Crafting an internal "Acceptable Use Policy" for employee interaction with Gemini.
- Future Trends: Preparing for Agentic Governance—how to manage AI agents that act autonomously.
- Final Project: Drafting a Corporate AI Governance Roadmap that balances rapid innovation with regulatory safety.
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